1. Article level

#import
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)

covid-science.txt

def load_data(path,file_name,seperator):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-15.txt",'NEW_PAPER')

Number of words in text file : 88023
Total Number of Documents: 15

def preprocess_data(doc_set):
    """
    Input  : docuemnt list
    Purpose: preprocess text (tokenize, removing stopwords, and stemming)
    Output : preprocessed text
    """
    # initialize regex tokenizer
    tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    en_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    p_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>4]
        print(tokens)
        # remove stop words from tokens
        stopped_tokens = [i for i in tokens if not i in en_stop]
        # stem tokens
        stemmed_tokens = [p_stemmer.stem(i) for i in stopped_tokens]
        # add tokens to list
        texts.append(stemmed_tokens)
    return texts

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'cases', 'imagesbecome', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'systems', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 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'project', 'usecase', 'volume', '42381o', 'catalá', 'analysis', 'public', 'image', 'datasets']

def prepare_corpus(doc_clean):
    """
    Input  : clean document
    Purpose: create term dictionary of our courpus and Converting list of documents (corpus) into Document Term Matrix
    Output : term dictionary and Document Term Matrix
    """
    # Creating the term dictionary of our courpus, where every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    doc_term_matrix = [dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return dictionary,doc_term_matrix

def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : clean document, number of topics and number of words associated with each topic
    Purpose: create LSA model using gensim
    Output : return LSA model
    """
    dictionary,doc_term_matrix=prepare_corpus(doc_clean)
    # generate LSA model
    lsamodel = LsiModel(doc_term_matrix, num_topics=number_of_topics, id2word = dictionary)  # train model
    #print(lsamodel.print_topics(num_topics=number_of_topics, num_words=words))
    return lsamodel

# LSA Model
number_of_topics=15
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_articles = create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_science.show_topic(0, topn=5))

{'covid': 0.48404637328080685,
 'imag': 0.34556353402865697,
 'model': 0.29152531367714396,
 'dataset': 0.1920788517016384,
 'class': 0.16833626183973904}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph21.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled24.png')

cosine-similarity
             covid       imag      model    dataset      class
covid            1   0.117954   0.182831 -0.0841955   0.510676
imag      0.117954          1  -0.124147   0.320962  0.0668724
model     0.182831  -0.124147          1 -0.0262809  0.0105534
dataset -0.0841955   0.320962 -0.0262809          1  0.0909586
class     0.510676  0.0668724  0.0105534  0.0909586          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig24.png')

covid-psychology-large.txt

document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt",'NEW PAPER')

Number of words in text file : 58236
Total Number of Documents: 15

clean_text=preprocess_data(document_list)

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['special', 'section', 'papercovid', 'context', 'people', 'inemergencies', 'probably', 'because', 'ofcollective', 'psychologyjohn', 'drury1', 'stephen', 'reicher2', 'clifford', 'stott31university', 'sussex', 'brighton', 'uk2university', 'andrews', 'uk3keele', 'university', 'uknotions', 'psychological', 'frailty', 'forefront', 'debates', 'around', 'publicresponse', 'covid', 'pandemic', 'particular', 'there', 'argument', 'collectiveselfishness', 'thoughtless', 'behaviour', 'reaction', 'would', 'effects', 'covid', 'worse', 'kinds', 'claims', 'relation', 'other', 'kinds', 'ofemergencies', 'fires', 'earthquakes', 'sinking', 'ships', 'argue', 'these', 'cases', 'aswell', 'covid', 'pandemic', 'other', 'factors', 'better', 'explanations', 'forfatalities', 'namely', 'under', 'reaction', 'threat', 'systemic', 'structural', 'factors', 'andmismanagement', 'psychologizing', 'disasters', 'serves', 'distract', 'causes', 'andthus', 'might', 'responsible', 'being', 'problem', 'collectivebehaviour', 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'disadvantaged', 'suffer', 'disproportionately', 'thegrenfell', 'tower', 'worst', 'united', 'kingdom', 'since', 'second', 'world', 'neglect', 'saving', 'authorities', 'manufacturers', 'behind', 'fataldecision', 'block', 'flammable', 'material', 'poorer', 'sections', 'society', 'fewerresources', 'disaster', 'strikes', 'power', 'demand', 'adequateaftercare', 'third', 'reason', 'emergencies', 'often', 'badly', 'mismanagement', 'cocoanutgrove', 'nightclub', 'which', 'people', 'presented', 'psychologytextbooks', 'embodiment', 'received', 'wisdom', 'deaths', 'night', 'firesare', 'crowd', 'panic', 'chertkoff', 'kushigian', 'detailed', 'analysis', 'eventssuggests', 'instead', 'failure', 'management', 'types', 'first', 'there', 'mismanagement', 'ofspace', 'emergency', 'locked', 'windows', 'nailed', 'toprevent', 'people', 'leaving', 'without', 'paying', 'their', 'second', 'there', 'failures', 'ofcommunication', 'there', 'signs', 'training', 'emergency', 'evacuation', 'whenstaff', 'tried', 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'thatpeople', 'compete', 'coordinate', 'evacuations', 'positionedpsychologically', 'individuals', 'rather', 'group', 'members', 'mintz', 'shows', 'evacuating', 'crowd', 'blocks', 'explained', 'terms', 'theprevalence', 'individual', 'competition', 'collective', 'setting', 'rather', 'terms', 'ofexcessive', 'emotion', 'these', 'cases', 'emergency', 'badly', 'absence', 'ofcollective', 'psychology', 'coordination', 'cooperation', 'cooperating', 'giving', 'support', 'carry', 'risks', 'which', 'acknowledged', 'evacuations', 'larger', 'group', 'slower', 'egress', 'because', 'speed', 'reducedthrough', 'people', 'interacting', 'other', 'aguirre', 'motivation', 'togive', 'support', 'other', 'survivors', 'risks', 'their', 'personal', 'safety', 'collective', 'emergency', 'always', 'particular', 'individuals', 'drury', 'cocking', 'reicher', 'covid', 'individualfrom', 'supporting', 'group', 'clear', 'where', 'supportive', 'behaviour', 'involves', 'physicalproximity', 'whether', 'delivering', 'giving', 'emotional', 'support', 'about', 'public', 'under', 'reaction', 'occur', 'under', 'estimation', 'hassometimes', 'characterized', 'optimistic', 'kinsey', 'gwynne', 'kuligowski', 'kinateder', 'context', 'where', 'emergency', 'events', 'thetime', 'reasonable', 'assume', 'happen', 'assumption', 'reversewhen', 'emergency', 'events', 'become', 'common', 'example', 'after', 'spate', 'ofterrorist', 'attacks', 'london', 'hundreds', 'people', 'oxford', 'street', 'noise', 'thatturned', 'harmless', 'general', 'extent', 'which', 'information', 'concerning', 'athreat', 'plausible', 'function', 'broad', 'social', 'context', 'dangers', 'expectationsof', 'danger', 'raised', 'readiness', 'other', 'action', 'greater', 'context', 'ofrecent', 'incidents', 'relevant', 'social', 'group', 'perceptions', 'become', 'collective', 'people', 'respond', 'direct', 'signals', 'other', 'people', 'responses', 'signal', 'bruder', 'fischer', 'manstead', 'suggest', 'extent', 'which', 'response', 'others', 'possible', 'threat', 'isseen', 'conveying', 'information', 'dependent', 'relevance', 'these', 'others', 'aparticular', 'context', 'which', 'often', 'function', 'shared', 'identity', 'based', 'weknow', 'about', 'social', 'influence', 'processes', 'other', 'contexts', 'bruder', 'caseof', 'covid', 'plausible', 'suppose', 'sight', 'others', 'community', 'routinelyobserving', 'ignoring', 'social', 'distancing', 'regulations', 'example', 'likely', 'strongsignal', 'around', 'safety', 'doing', 'particularly', 'where', 'identify', 'thecommunity', 'these', 'exemplars', 'prototypes', 'psychological', 'factors', 'interact', 'management', 'failures', 'explain', 'someemergency', 'events', 'badly', 'fearing', 'public', 'panic', 'leads', 'authorities', 'withholdinformation', 'about', 'emergency', 'drury', 'information', 'anemergency', 'increases', 'public', 'anxiety', 'public', 'perceives', 'information', 'isbeing', 'withheld', 'damages', 'their', 'relationship', 'authority', 'carteret', 'consequently', 'authorities', 'release', 'correct', 'information', 'thepublic', 'mistrust', 'covid', 'treat', 'thepublic', 'respect', 'order', 'build', 'trust', 'advice', 'given', 'behaviouralscientists', 'government', '24discussion', 'conclusionswe', 'emergencies', 'people', 'behave', 'selfishly', 'thoughtlessly', 'orthat', 'react', 'indeed', 'explained', 'research', 'suggests', 'conditionsfor', 'competition', 'prevail', 'cooperation', 'questioning', 'notionthat', 'public', 'reactions', 'default', 'major', 'cause', 'problems', 'covid', '19crisis', 'existing', 'literature', 'disasters', 'support', 'prima', 'faciemajor', 'problems', 'covid', 'response', 'outcomes', 'better', 'understoodotherwise', 'terms', 'political', 'under', 'reaction', 'systemic', 'issues', 'mismanagement', 'collective', 'panic', 'referred', 'disaster', 'literature', 'disasters', 'druryet', 'rather', 'neutral', 'description', 'people', 'actually', 'behave', 'bestunderstood', 'particular', 'discourse', 'cultural', 'representation', 'whichpsychologizes', 'indeed', 'pathologizes', 'public', 'responses', 'emergencies', 'disasters', 'given', 'known', 'about', 'under', 'reaction', 'systemic', 'factors', 'mismanagement', 'inemergencies', 'emphasize', 'instead', 'collective', 'behaviour', 'clearideological', 'functions', 'naturalizing', 'fatalities', 'distracts', 'causes', 'thusfrom', 'might', 'responsible', 'mismanagement', 'instead', 'blaming', 'victims', 'irony', 'course', 'being', 'problem', 'collective', 'psychology', 'inemergencies', 'solidarity', 'cooperation', 'commonly', 'witnessed', 'among', 'communitymembers', 'strangers', 'usually', 'solution', 'collective', 'psychology', 'therefore', 'andshould', 'harnessed', 'effectively', 'policy', 'practice', 'covid', 'response', 'elcheroth', 'drury', 'through', 'framing', 'threat', 'solution', 'collectiveterms', 'through', 'emphasizing', 'shared', 'norms', 'around', 'collective', 'being', 'safety', 'drury', 'people', 'emergencies', 'probably', 'because', 'ofcollective', 'psychology']

# LSA Model
number_of_topics=15
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_articles=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_psy.show_topic(0,topn=5))

{'covid': 0.4241833010171727,
 'psycholog': 0.276494658493425,
 'group': 0.25392735028600955,
 'pandem': 0.22421308419475472,
 'health': 0.2080533594503455}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph22.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled25.png')

cosine-similarity
              covid   psycholog       group     pandem     health
covid             1    0.235744    0.032874    0.48363    0.12375
psycholog  0.235744           1  0.00600425   0.355838   0.359423
group      0.032874  0.00600425           1 -0.0361535 -0.0297868
pandem      0.48363    0.355838  -0.0361535          1   0.466249
health      0.12375    0.359423  -0.0297868   0.466249          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig25.png')

def mantel_test(n):
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))

    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_psy = dict(model_psy.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)

    b={} #non law words in law
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

mantel_test(5)

Pairwise-distance
              covid    psycholog     group    pandem    health      imag  \
covid             0     0.627345  0.616418  0.634626  0.599659  0.906064   
psycholog  0.627345  1.16415e-10  0.066445  0.196729  0.149188  0.731796   
group      0.616418     0.066445         0  0.200285  0.161369  0.717135   
pandem     0.634626     0.196729  0.200285         0  0.198709  0.785046   
health     0.599659     0.149188  0.161369  0.198709         0  0.762977   
imag       0.906064     0.731796  0.717135  0.785046  0.762977         0   
model       0.83963     0.684718    0.7009   0.68419  0.696724   1.06205   
dataset    0.901475     0.596784  0.616381  0.636306  0.612924  0.781713   
class      0.560897      0.47378  0.504815  0.554316  0.498453  0.844705   

              model   dataset     class  
covid       0.83963  0.901475  0.560897  
psycholog  0.684718  0.596784   0.47378  
group        0.7009  0.616381  0.504815  
pandem      0.68419  0.636306  0.554316  
health     0.696724  0.612924  0.498453  
imag        1.06205  0.781713  0.844705  
model             0  0.919469  0.828103  
dataset    0.919469         0  0.727202  
class      0.828103  0.727202         0  
Pairwise-distance
              covid psycholog        group    pandem    health         imag  \
covid             0  0.665039     0.949824  0.512287  0.798946     0.569647   
psycholog  0.665039         0     0.916289  0.500813  0.645733     0.505794   
group      0.949824  0.916289  1.49012e-08  0.869534   1.01224     0.778182   
pandem     0.512287  0.500813     0.869534         0  0.561819     0.365353   
health     0.798946  0.645733      1.01224  0.561819         0     0.627794   
imag       0.569647  0.505794     0.778182  0.365353  0.627794  6.58545e-10   
model      0.545144  0.519223     0.800488  0.386536  0.641427      0.13642   
dataset    0.574902  0.496188     0.774211  0.371155   0.62841    0.0360889   
class      0.577615  0.497861     0.771704  0.373614  0.631885    0.0460422   

              model      dataset      class  
covid      0.545144     0.574902   0.577615  
psycholog  0.519223     0.496188   0.497861  
group      0.800488     0.774211   0.771704  
pandem     0.386536     0.371155   0.373614  
health     0.641427      0.62841   0.631885  
imag        0.13642    0.0360889  0.0460422  
model             0     0.122277   0.134249  
dataset    0.122277  2.85158e-10  0.0254673  
class      0.134249    0.0254673          0  

(-0.5583274383531106, 0.9722, -1.7668867248811133)

mantel_test(10)

Pairwise-distance
              covid    psycholog      group    pandem     health        peopl  \
covid             0     0.627345   0.616418  0.634626   0.599659     0.661675   
psycholog  0.627345  1.16415e-10   0.066445  0.196729   0.149188     0.293753   
group      0.616418     0.066445          0  0.200285   0.161369     0.298513   
pandem     0.634626     0.196729   0.200285         0   0.198709     0.326716   
health     0.599659     0.149188   0.161369  0.198709          0     0.330524   
peopl      0.661675     0.293753   0.298513  0.326716   0.330524  7.45058e-09   
impact      0.61597       0.0638  0.0972342  0.222891   0.142905     0.289812   
social     0.737109     0.430898   0.420895   0.40153   0.430305     0.394205   
studi      0.586486     0.164695   0.161258  0.247848   0.187599     0.313501   
research   0.595436    0.0901519   0.120434  0.201167  0.0975713     0.322663   
imag       0.906064     0.731796   0.717135  0.785046   0.762977     0.835098   
model       0.83963     0.684718     0.7009   0.68419   0.696724     0.758934   
dataset    0.901475     0.596784   0.616381  0.636306   0.612924     0.655583   
class      0.560897      0.47378   0.504815  0.554316   0.498453     0.578072   
perform    0.652411     0.356799   0.355504  0.423214    0.38486     0.449317   
learn      0.735845     0.516397   0.514441  0.524195   0.497694     0.623015   
detect     0.627337     0.325566   0.337485  0.425285   0.389054     0.432081   
use        0.532273     0.223613   0.195537  0.314649   0.272496     0.344397   
base       0.505415     0.210683   0.218844  0.264391   0.282322     0.372698   

                impact    social     studi   research      imag     model  \
covid          0.61597  0.737109  0.586486   0.595436  0.906064   0.83963   
psycholog       0.0638  0.430898  0.164695  0.0901519  0.731796  0.684718   
group        0.0972342  0.420895  0.161258   0.120434  0.717135    0.7009   
pandem        0.222891   0.40153  0.247848   0.201167  0.785046   0.68419   
health        0.142905  0.430305  0.187599  0.0975713  0.762977  0.696724   
peopl         0.289812  0.394205  0.313501   0.322663  0.835098  0.758934   
impact     1.31709e-09  0.443311  0.166424  0.0962653  0.736111  0.693722   
social        0.443311         0  0.384823   0.452931  0.896969  0.817203   
studi         0.166424  0.384823         0   0.192156  0.715145   0.69514   
research     0.0962653  0.452931  0.192156          0  0.731434  0.691281   
imag          0.736111  0.896969  0.715145   0.731434         0   1.06205   
model         0.693722  0.817203   0.69514   0.691281   1.06205         0   
dataset       0.590031  0.690754  0.579846   0.595966  0.781713  0.919469   
class         0.461026  0.672743  0.476091   0.468226  0.844705  0.828103   
perform       0.377568  0.586466   0.34259   0.372694  0.812935  0.551424   
learn         0.482763  0.703538  0.471397   0.522492  0.905108  0.815302   
detect        0.328156  0.569479  0.386567   0.323053  0.681579  0.653473   
use           0.222522  0.510654  0.243858   0.243918  0.732506  0.728691   
base          0.224732  0.493317  0.255434   0.218313  0.723226  0.663673   

            dataset     class   perform     learn    detect       use  \
covid      0.901475  0.560897  0.652411  0.735845  0.627337  0.532273   
psycholog  0.596784   0.47378  0.356799  0.516397  0.325566  0.223613   
group      0.616381  0.504815  0.355504  0.514441  0.337485  0.195537   
pandem     0.636306  0.554316  0.423214  0.524195  0.425285  0.314649   
health     0.612924  0.498453   0.38486  0.497694  0.389054  0.272496   
peopl      0.655583  0.578072  0.449317  0.623015  0.432081  0.344397   
impact     0.590031  0.461026  0.377568  0.482763  0.328156  0.222522   
social     0.690754  0.672743  0.586466  0.703538  0.569479  0.510654   
studi      0.579846  0.476091   0.34259  0.471397  0.386567  0.243858   
research   0.595966  0.468226  0.372694  0.522492  0.323053  0.243918   
imag       0.781713  0.844705  0.812935  0.905108  0.681579  0.732506   
model      0.919469  0.828103  0.551424  0.815302  0.653473  0.728691   
dataset           0  0.727202  0.692372  0.781132  0.696688   0.61537   
class      0.727202         0  0.624976  0.691328  0.577896  0.521271   
perform    0.692372  0.624976         0  0.577586  0.429273  0.372983   
learn      0.781132  0.691328  0.577586         0    0.6373    0.5271   
detect     0.696688  0.577896  0.429273    0.6373         0  0.381119   
use         0.61537  0.521271  0.372983    0.5271  0.381119         0   
base       0.649234  0.467935  0.389069  0.542538  0.273419  0.249719   

               base  
covid      0.505415  
psycholog  0.210683  
group      0.218844  
pandem     0.264391  
health     0.282322  
peopl      0.372698  
impact     0.224732  
social     0.493317  
studi      0.255434  
research   0.218313  
imag       0.723226  
model      0.663673  
dataset    0.649234  
class      0.467935  
perform    0.389069  
learn      0.542538  
detect     0.273419  
use        0.249719  
base              0  
Pairwise-distance
              covid psycholog        group    pandem    health     peopl  \
covid             0  0.665039     0.949824  0.512287  0.798946  0.722569   
psycholog  0.665039         0     0.916289  0.500813  0.645733  0.689255   
group      0.949824  0.916289  1.49012e-08  0.869534   1.01224  0.778218   
pandem     0.512287  0.500813     0.869534         0  0.561819  0.587776   
health     0.798946  0.645733      1.01224  0.561819         0  0.846159   
peopl      0.722569  0.689255     0.778218  0.587776  0.846159         0   
impact     0.556492  0.538323     0.846016   0.33904  0.661179  0.576973   
social     0.645589  0.607703     0.734502  0.521936  0.690599  0.479194   
studi      0.428616  0.526824     0.775837  0.477865  0.719016  0.652853   
research   0.718988  0.462598     0.925893  0.676573  0.830778  0.763131   
imag       0.569647  0.505794     0.778182  0.365353  0.627794   0.49626   
model      0.545144  0.519223     0.800488  0.386536  0.641427  0.507928   
dataset    0.574902  0.496188     0.774211  0.371155   0.62841  0.494985   
class      0.577615  0.497861     0.771704  0.373614  0.631885  0.482538   
perform    0.617221  0.527324     0.807069  0.408744  0.667675  0.544408   
learn      0.565854  0.512273     0.781439  0.339772  0.639181  0.501184   
detect     0.573687   0.49589     0.775558  0.367129  0.630211  0.497541   
use        0.587577  0.463649     0.802866  0.368907  0.663655  0.503238   
base       0.538749  0.517056     0.804437  0.351464  0.652988  0.521802   

             impact    social     studi  research         imag     model  \
covid      0.556492  0.645589  0.428616  0.718988     0.569647  0.545144   
psycholog  0.538323  0.607703  0.526824  0.462598     0.505794  0.519223   
group      0.846016  0.734502  0.775837  0.925893     0.778182  0.800488   
pandem      0.33904  0.521936  0.477865  0.676573     0.365353  0.386536   
health     0.661179  0.690599  0.719016  0.830778     0.627794  0.641427   
peopl      0.576973  0.479194  0.652853  0.763131      0.49626  0.507928   
impact            0  0.549137  0.420103  0.597067     0.333984   0.34889   
social     0.549137         0   0.49973  0.652226     0.376604    0.3623   
studi      0.420103   0.49973         0  0.490161     0.306665  0.325089   
research   0.597067  0.652226  0.490161         0     0.536332  0.559126   
imag       0.333984  0.376604  0.306665  0.536332  6.58545e-10   0.13642   
model       0.34889    0.3623  0.325089  0.559126      0.13642         0   
dataset    0.329749  0.387021  0.307446  0.529718    0.0360889  0.122277   
class      0.332517   0.39049  0.307044  0.530599    0.0460422  0.134249   
perform    0.391658   0.43571   0.38509  0.589779     0.205748  0.245633   
learn       0.31445  0.406851  0.322133  0.532634     0.104919  0.162804   
detect     0.330576   0.38691  0.303934  0.529531    0.0387109  0.126376   
use         0.37528  0.357205  0.343383  0.522696     0.152451  0.125648   
base       0.346954  0.376571  0.313572  0.509576      0.12651  0.206961   

               dataset      class   perform      learn       detect       use  \
covid         0.574902   0.577615  0.617221   0.565854     0.573687  0.587577   
psycholog     0.496188   0.497861  0.527324   0.512273      0.49589  0.463649   
group         0.774211   0.771704  0.807069   0.781439     0.775558  0.802866   
pandem        0.371155   0.373614  0.408744   0.339772     0.367129  0.368907   
health         0.62841   0.631885  0.667675   0.639181     0.630211  0.663655   
peopl         0.494985   0.482538  0.544408   0.501184     0.497541  0.503238   
impact        0.329749   0.332517  0.391658    0.31445     0.330576   0.37528   
social        0.387021    0.39049   0.43571   0.406851      0.38691  0.357205   
studi         0.307446   0.307044   0.38509   0.322133     0.303934  0.343383   
research      0.529718   0.530599  0.589779   0.532634     0.529531  0.522696   
imag         0.0360889  0.0460422  0.205748   0.104919    0.0387109  0.152451   
model         0.122277   0.134249  0.245633   0.162804     0.126376  0.125648   
dataset    2.85158e-10  0.0254673  0.204009  0.0969234    0.0161445   0.14166   
class        0.0254673          0  0.206249  0.0967058    0.0262711  0.146868   
perform       0.204009   0.206249         0   0.221897     0.205531  0.252859   
learn        0.0969234  0.0967058  0.221897          0    0.0863365  0.172499   
detect       0.0161445  0.0262711  0.205531  0.0863365  2.32831e-10  0.139091   
use            0.14166   0.146868  0.252859   0.172499     0.139091         0   
base          0.147138   0.152011  0.228732   0.122953     0.141612  0.211245   

               base  
covid      0.538749  
psycholog  0.517056  
group      0.804437  
pandem     0.351464  
health     0.652988  
peopl      0.521802  
impact     0.346954  
social     0.376571  
studi      0.313572  
research   0.509576  
imag        0.12651  
model      0.206961  
dataset    0.147138  
class      0.152011  
perform    0.228732  
learn      0.122953  
detect     0.141612  
use        0.211245  
base              0  

(-0.4187028966996108, 0.9848, -1.9122287293861753)

2. 2-3 paragraphs

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-15.txt")

Number of words in text file : 88023
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'medical', 'centers', 'hence', 'cases', 'images', 'become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'systems', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels', 'manuscript', 'received', 'september', 'revised', 'october', 'accepted', 'november']
['publication', 'november', 'current', 'version', 'december', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '18136', 'gómez', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'andalusian', 'research', 'institute', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'atlanttic', 'research', 'center']

# LSA Model
number_of_topics=2
words_nscience=len(clean_text[0])+len(clean_text[1])
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)
words_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_p=create_gensim_lsa_model(clean_text,number_of_topics,words_nscience)

words_nscience

238

dict(model_science.show_topic(0, topn=5))

{'support': -0.3289947178827894,
 'granada': -0.24814037209037276,
 'covid': -0.22769581399154298,
 'programm': -0.2193298119218596,
 'research': -0.2193298119218596}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph23.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn = words_nscience))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled26.png')

cosine-similarity
            support   granada      covid   programm   research
support           1  0.956969 -0.0794645          1          1
granada    0.956969         1   0.213226   0.956969   0.956969
covid    -0.0794645  0.213226          1 -0.0794645 -0.0794645
programm          1  0.956969 -0.0794645          1          1
research          1  0.956969 -0.0794645          1          1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig26.png')

covid-psycology.txt

document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt")

Number of words in text file : 58236
Total Number of Documents: 2

clean_text=preprocess_data(document_list)

['international', 'sociology', 'author', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '0268580920948807', 'journals', 'sagepub', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', 'covid', 'pandemic', 'outbreak', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated']

# LSA Model
number_of_topics=2
words_npsy=len(clean_text[0])+len(clean_text[1])
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_p = create_gensim_lsa_model(clean_text,number_of_topics,words_npsy)

dict(model_psy.show_topic(0, topn=5))

{'misinfodem': 0.5033743541941376,
 'covid': 0.3629711503323517,
 'articl': 0.21459251935197443,
 'commun': 0.14837863098037718,
 'mechan': 0.1483786309803767}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph24.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words_npsy))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled27.png')

cosine-similarity
           misinfodem     covid    articl    commun    mechan
misinfodem          1  0.976353  0.993795  0.755596  0.755596
covid        0.976353         1  0.946251  0.879335  0.879335
articl       0.993795  0.946251         1  0.678051  0.678051
commun       0.755596  0.879335  0.678051         1         1
mechan       0.755596  0.879335  0.678051         1         1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig27.png')

mantel_test(5)

Pairwise-distance
           misinfodem     covid    articl commun mechan   support    granada  \
misinfodem          0         0         0      0      0         0          0   
covid               0         0  0.378443      0      0  0.628979    0.47364   
articl              0  0.378443         0      0      0  0.423546   0.266413   
commun              0         0         0      0      0         0          0   
mechan              0         0         0      0      0         0          0   
support             0  0.628979  0.423546      0      0         0   0.168269   
granada             0   0.47364  0.266413      0      0  0.168269          0   
programm            0  0.545218  0.293443      0      0  0.133207  0.0775087   
research            0  0.545218  0.293443      0      0  0.133207  0.0775087   

             programm   research  
misinfodem          0          0  
covid        0.545218   0.545218  
articl       0.293443   0.293443  
commun              0          0  
mechan              0          0  
support      0.133207   0.133207  
granada     0.0775087  0.0775087  
programm            0          0  
research            0          0  
Pairwise-distance
           misinfodem     covid    articl    commun    mechan support granada  \
misinfodem          0  0.191325  0.296807  0.422574  0.422574       0       0   
covid        0.191325         0  0.160587  0.236438  0.236438       0       0   
articl       0.296807  0.160587         0  0.173786  0.173786       0       0   
commun       0.422574  0.236438  0.173786         0         0       0       0   
mechan       0.422574  0.236438  0.173786         0         0       0       0   
support             0         0         0         0         0       0       0   
granada             0         0         0         0         0       0       0   
programm            0         0         0         0         0       0       0   
research     0.533903  0.353741    0.2551  0.121646  0.121646       0       0   

           programm  research  
misinfodem        0  0.533903  
covid             0  0.353741  
articl            0    0.2551  
commun            0  0.121646  
mechan            0  0.121646  
support           0         0  
granada           0         0  
programm          0         0  
research          0         0  

(-0.12443276806076488, 0.6549, -0.511434675696413)

mantel_test(10)

Pairwise-distance
           misinfodem     covid    articl commun mechan health strategi  \
misinfodem          0         0         0      0      0      0        0   
covid               0         0  0.378443      0      0      0        0   
articl              0  0.378443         0      0      0      0        0   
commun              0         0         0      0      0      0        0   
mechan              0         0         0      0      0      0        0   
health              0         0         0      0      0      0        0   
strategi            0         0         0      0      0      0        0   
psycholog           0         0         0      0      0      0        0   
misinform           0         0         0      0      0      0        0   
public              0  0.465052  0.112805      0      0      0        0   
support             0  0.628979  0.423546      0      0      0        0   
granada             0   0.47364  0.266413      0      0      0        0   
programm            0  0.545218  0.293443      0      0      0        0   
research            0  0.545218  0.293443      0      0      0        0   
tabik               0  0.436747   0.19981      0      0      0        0   
garcía              0  0.331184  0.138524      0      0      0        0   
project             0  0.511267   0.22973      0      0      0        0   
decsai              0  0.511267   0.22973      0      0      0        0   
gómez               0  0.407471  0.133207      0      0      0        0   

           psycholog misinform     public   support    granada   programm  \
misinfodem         0         0          0         0          0          0   
covid              0         0   0.465052  0.628979    0.47364   0.545218   
articl             0         0   0.112805  0.423546   0.266413   0.293443   
commun             0         0          0         0          0          0   
mechan             0         0          0         0          0          0   
health             0         0          0         0          0          0   
strategi           0         0          0         0          0          0   
psycholog          0         0          0         0          0          0   
misinform          0         0          0         0          0          0   
public             0         0          0  0.333016   0.197705    0.19981   
support            0         0   0.333016         0   0.168269   0.133207   
granada            0         0   0.197705  0.168269          0  0.0775087   
programm           0         0    0.19981  0.133207  0.0775087          0   
research           0         0    0.19981  0.133207  0.0775087          0   
tabik              0         0   0.138524   0.22973  0.0666033   0.112805   
garcía             0         0   0.154608  0.336537   0.168269    0.22561   
project            0         0   0.133207   0.19981  0.0903451  0.0666033   
decsai             0         0   0.133207   0.19981  0.0903451  0.0666033   
gómez              0         0  0.0903451  0.293443   0.133207   0.168269   

             research      tabik     garcía    project     decsai      gómez  
misinfodem          0          0          0          0          0          0  
covid        0.545218   0.436747   0.331184   0.511267   0.511267   0.407471  
articl       0.293443    0.19981   0.138524    0.22973    0.22973   0.133207  
commun              0          0          0          0          0          0  
mechan              0          0          0          0          0          0  
health              0          0          0          0          0          0  
strategi            0          0          0          0          0          0  
psycholog           0          0          0          0          0          0  
misinform           0          0          0          0          0          0  
public        0.19981   0.138524   0.154608   0.133207   0.133207  0.0903451  
support      0.133207    0.22973   0.336537    0.19981    0.19981   0.293443  
granada     0.0775087  0.0666033   0.168269  0.0903451  0.0903451   0.133207  
programm            0   0.112805    0.22561  0.0666033  0.0666033   0.168269  
research            0   0.112805    0.22561  0.0666033  0.0666033   0.168269  
tabik        0.112805          0   0.112805  0.0775087  0.0775087  0.0666033  
garcía        0.22561   0.112805          0   0.181739   0.181739  0.0775087  
project     0.0666033  0.0775087   0.181739          0          0   0.112805  
decsai      0.0666033  0.0775087   0.181739          0          0   0.112805  
gómez        0.168269  0.0666033  0.0775087   0.112805   0.112805          0  
Pairwise-distance
           misinfodem     covid     articl     commun     mechan     health  \
misinfodem          0  0.191325   0.296807   0.422574   0.422574   0.422574   
covid        0.191325         0   0.160587   0.236438   0.236438   0.236438   
articl       0.296807  0.160587          0   0.173786   0.173786   0.173786   
commun       0.422574  0.236438   0.173786          0          0          0   
mechan       0.422574  0.236438   0.173786          0          0          0   
health       0.422574  0.236438   0.173786          0          0          0   
strategi     0.364939   0.24088  0.0802933   0.191551   0.191551   0.191551   
psycholog    0.364939   0.24088  0.0802933   0.191551   0.191551   0.191551   
misinform    0.510199  0.318875   0.290918   0.118949   0.118949   0.118949   
public        0.41268  0.243293    0.12755  0.0789346  0.0789346  0.0789346   
support             0         0          0          0          0          0   
granada             0         0          0          0          0          0   
programm            0         0          0          0          0          0   
research     0.533903  0.353741     0.2551   0.121646   0.121646   0.121646   
tabik               0         0          0          0          0          0   
garcía              0         0          0          0          0          0   
project             0         0          0          0          0          0   
decsai              0         0          0          0          0          0   
gómez               0         0          0          0          0          0   

             strategi  psycholog misinform     public support granada  \
misinfodem   0.364939   0.364939  0.510199    0.41268       0       0   
covid         0.24088    0.24088  0.318875   0.243293       0       0   
articl      0.0802933  0.0802933  0.290918    0.12755       0       0   
commun       0.191551   0.191551  0.118949  0.0789346       0       0   
mechan       0.191551   0.191551  0.118949  0.0789346       0       0   
health       0.191551   0.191551  0.118949  0.0789346       0       0   
strategi            0          0   0.30895   0.118949       0       0   
psycholog           0          0   0.30895   0.118949       0       0   
misinform     0.30895    0.30895         0   0.191551       0       0   
public       0.118949   0.118949  0.191551          0       0       0   
support             0          0         0          0       0       0   
granada             0          0         0          0       0       0   
programm            0          0         0          0       0       0   
research     0.233213   0.233213  0.145904    0.12755       0       0   
tabik               0          0         0          0       0       0   
garcía              0          0         0          0       0       0   
project             0          0         0          0       0       0   
decsai              0          0         0          0       0       0   
gómez               0          0         0          0       0       0   

           programm  research tabik garcía project decsai gómez  
misinfodem        0  0.533903     0      0       0      0     0  
covid             0  0.353741     0      0       0      0     0  
articl            0    0.2551     0      0       0      0     0  
commun            0  0.121646     0      0       0      0     0  
mechan            0  0.121646     0      0       0      0     0  
health            0  0.121646     0      0       0      0     0  
strategi          0  0.233213     0      0       0      0     0  
psycholog         0  0.233213     0      0       0      0     0  
misinform         0  0.145904     0      0       0      0     0  
public            0   0.12755     0      0       0      0     0  
support           0         0     0      0       0      0     0  
granada           0         0     0      0       0      0     0  
programm          0         0     0      0       0      0     0  
research          0         0     0      0       0      0     0  
tabik             0         0     0      0       0      0     0  
garcía            0         0     0      0       0      0     0  
project           0         0     0      0       0      0     0  
decsai            0         0     0      0       0      0     0  
gómez             0         0     0      0       0      0     0  

(-0.15851516925283998, 0.8535, -1.0488047694214804)

Paragraph level

covid-science.txt

def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)

document_list,titles,word_count_science=load_data("","covid-science-15.txt")

Number of words in text file : 88023
Total Number of Documents: 10436

clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'martín', 'rodríguez', 'sevillano', 'garcía', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
['infectious', 'diseases', 'century', 'diagnosed', 'using', 'testing', 'scans']
['chest', 'images', 'computed', 'tomography']
['scanners', 'testing', 'available']
['medical', 'centers', 'hence', 'cases', 'images']
['become', 'effective', 'assisting', 'clinicians', 'making', 'decisions', 'learning', 'neural', 'networks']
['great', 'potential', 'building', 'covid', 'triage', 'systems']
['detecting', 'covid', 'patients', 'especially', 'patients']
['severity', 'unfortunately', 'current', 'databases', 'allow']
['building', 'systems', 'highly', 'heterogeneous']
['biased', 'towards', 'severe', 'cases', 'article', 'threefold', 'demystify', 'sensitivities', 'achieved']
['recent', 'covid', 'classification', 'models', 'under']
['close', 'collaboration', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'includes', 'levels']
['manuscript', 'received', 'september', 'revised', 'october']
['accepted', 'november', 'publication', 'november']
['current', 'version', 'december', 'supported']
['project', 'deepscop', 'ayudas', 'fundación', 'equipos', 'investigación', 'científica', 'covid19_rx', 'ayudas', 'fundación']
['equipos', 'investigación', 'científica', 'covid']
['spanish', 'ministry', 'science', 'technology', 'under']
['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal']
['programme', '18136', 'gómez', 'supported']
['programme', 'fpu16', '04765', 'charte', 'supported']
['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research']
['council', 'grant', 'agreement', '647038', 'biodesert', 'project']
['approved', 'provincial', 'research', 'ethics', 'committee', 'granada']
['corresponding', 'author', 'siham', 'tabik']
['tabik', 'gómez', 'sevillano', 'garcía', 'charte', 'suárez']
['luengo', 'herrera', 'andalusian', 'research', 'institute']
['science', 'computational', 'intelligence', 'university', 'granada']
['18071', 'granada', 'spain', 'siham', 'anabelgrios', 'decsai']
['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz']
['julianlm', 'decsai', 'herrera', 'decsai']
['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', '36310', 'spain', 'joseluismartin']
['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail']
['euolm', 'yahoo']
['atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'galicia', 'spain']
['mreyarea', 'gmail']
['guirado', 'multidisciplinary', 'institute', 'environment']
['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain']
['geesecillo', 'gmail']
['digital', 'object', 'identifier', '3037127']
['severity', 'normal', 'positive', 'moderate', 'severe', 'covidgr', 'contains', 'positive']
['negative', 'posteroanterior', 'views']
['propose', 'covid', 'smart', 'based', 'network', 'covid', 'sdnet']
['methodology', 'improving', 'generalization', 'capacity']
['covid', 'classification', 'models', 'approach', 'reaches']
['stable', 'results', 'accuracy']
['severe', 'moderate']
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[]
[]
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['datasets', 'achieve', 'balanced', 'combination', 'adding', 'positive']
['pneumonia', 'observations', 'dataset', 'latter', 'highly', 'unbalanced', 'dataset', 'ative', 'positive', 'observations', 'after', 'process']
['segmentation', 'validity', 'filters', 'could', 'considered']
['positive', 'samples', 'another', 'dataset']
['needless', 'images', 'distinct']
['features', 'allow', 'classifier', 'apart']
['chexpert', 'example', 'including', 'large', 'proportion', 'images']
['particular', 'equipment', 'brand', 'model', 'system']
['learn', 'classify', 'images', 'equipment', 'positive']
['regardless', 'image', 'content', 'could', 'related']
['disease']
['additionally', 'simulated', 'combination']
['covid', 'control', 'datasets', 'evaluated', 'their']
['proposed', 'method', 'particular', 'datasets', 'bined', 'positive', 'covid', 'cases', 'covidcxr']
['chexpert', 'negative', 'control', 'samples', 'covidcxr', 'built']
['datasets', 'different', 'origins', 'hence', 'experiment', 'illus', 'trates', 'likely', 'problematic', 'effects', 'heterogeneous']
['combinations']
['based', 'methodology', 'probes', 'discrimination']
['induced', 'outside', 'lungs', 'expectations', 'about', 'results']
['experiment', 'there', 'dataset']
['background', 'expansion', 'could', 'increase', 'accuracy']
['accuracy', 'occluding', 'lungs', 'should', 'differ']
['significantly', 'results', 'follow']
['these', 'predictions', 'hypothesis', 'would', 'confirmed']
['results']
['background', 'expansion', 'exclusion']
['study']
['previous', 'section', 'proposed', 'examine', 'perfor', 'mance', 'classification', 'experiments', 'varying', 'addition']
['background', 'reduction', 'expected']
['results', 'first', 'biased', 'dataset', 'where']
['background', 'added', 'initial', 'images']
['classification', 'stays', 'constant', 'almost', 'stant', 'possible', 'imprecise', 'segmentation', 'other', 'perturbations', 'disease', 'information', 'already']
['present', 'beginning']
['second', 'scenario', 'accuracy', 'should', 'potentially']
['value', 'achieved', 'network']
['complete', 'image', 'value', 'close']
['lungs', 'completely', 'removed', 'necessarily']
['42376', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets']
['figure', 'background', 'expansion', 'exclusion', 'original', 'contour', 'shown', 'expanded', 'contour', 'green']
['contour', 'removed', 'shown', 'yellow']
['figure', 'analysis', 'workflow']
['linear', 'shown', 'graphs', 'straight']
['right', 'figure', 'offer', 'simplified']
['graphical', 'representation', 'expected', 'behavior']
['figure', 'green', 'represents', 'classification']
['obtained', 'using']
['analysis', 'performed', 'three', 'datasets']
['first', 'figure', 'bimcv', 'clearly', 'shows']
['significant', 'within', 'classification']
['steadily', 'increases', 'background', 'expansion']
['second', 'graph', 'shows', 'removing']
['associated', 'significant', 'decrease', 'accuracy']
['should', 'complete', 'exclusion']
['lungs', 'classifier', 'achieves', 'almost']
['second', 'figure', 'displays']
['slightly', 'lower', 'still', 'consistent', 'within']
['graphs', 'however', 'dataset', 'harder']
['segment', 'other']
['variability', 'shown', 'could', 'arise', 'poorly', 'segmented']
['images', 'nonetheless', 'achieved']
['lungs', 'completely', 'occluded', 'which']
['expected']
['third', 'figure', 'chexpert', 'conveys', 'inter', 'esting', 'results', 'graph', 'trend', 'expected']
['unbiased', 'dataset', 'doesn', 'along']
['background', 'expansion', 'nevertheless', 'precision']
['achieved', 'completely', 'occluded']
['around', 'implies']
['located', 'specifically', 'background']
['whole', 'image']
['combination', 'study']
['mentioned', 'before', 'combination', 'study', 'seeks', 'evaluate']
['combination', 'datasets', 'might', 'provoke', 'creation']
['biased', 'methodology', 'proposed', 'detect']
['these', 'weaknesses', 'final', 'collection']
['experiments', 'section', 'reproduced']
['using', 'combined', 'dataset', 'figure', 'shows', 'effect']
['varying', 'background', 'expansion', 'exclusion']
['combination', 'designed', 'balance', 'chexpert']
['cases', 'control', 'positive', 'pneumonia', 'images']
['chexpert', 'positive', 'images', 'giving']
['balanced', 'dataset', 'observations', 'class']
['experiment', 'explored', 'combination', 'images']
['control', 'patients', 'chexpert', 'whole']
['covid', 'images', 'covidcxr', 'dataset']
['combination', 'typical', 'recent', 'crisis', 'scenario', 'where']
['images', 'disease', 'available', 'obtained']
['different', 'locations', 'under', 'uncontrolled', 'conditions']
['different', 'equipment', 'acquisition', 'protocols']
['worst', 'scenario', 'results', 'accordance']
['figure']
['results', 'these', 'experiments', 'similar', 'fashion']
['chexpert', 'ubiquitous']
['volume', '42377o', 'catalá', 'analysis', 'public', 'image', 'datasets']
['figure', 'accuracy', 'function', 'background', 'expansion', 'reduction', 'green', 'dotted', 'lines']
['correct', 'behavior', 'biased', 'dataset', 'background', 'included']
['dotted', 'lines', 'indicate', 'expected', 'reduction', 'classification', 'lungs', 'removed']
['analysis', 'lines', 'accuracy', 'given', 'expansion', 'reduction', 'vertical', 'indicating']
['standard', 'deviation']
['image', 'despite', 'increasing', 'amount', 'background', 'inside']
['images', 'doesn', 'affect', 'accuracy', 'effect']
['occlusion', 'remarkable', 'within', 'results']
['discussion']
['learning', 'receiving', 'attention']
['powerful', 'methodology', 'analyzing', 'medical']
['images', 'ability', 'convolutional', 'neural', 'works', 'obtain', 'excellent', 'results']
['blackbox', 'opposed', 'classical', 'design']
['algorithms', 'attracted', 'researchers']
['works', 'using', 'covid', 'detection']
['images', 'report', 'accuracies', 'variety', 'network']
['architectures', 'particular', 'studies', 'using', 'vgg16', 'report']
['accuracy', 'dataset', 'built', 'covid']
['control', 'samples', 'accuracy', 'obtained']
['dataset', 'composed', 'covid', 'images', 'pneumo', 'negative', 'accuracy', 'achieved']
['using', 'dataset', 'contains', 'covid', 'images']
['pneumonia', 'negative', 'vgg16', 'achieves']
['results', 'detecting', 'pulmonary', 'diseases', 'strengthens']
['hypothesis', 'features', 'extracted', 'network']
['relevant', 'therefore', 'detected']
['experiments', 'related', 'within', 'images']
['drawbacks', 'often']
['large', 'amounts', 'learn', 'while', 'generic']
['databases', 'available', 'public', 'existing', 'covid', 'datasets']
['composed', 'images', 'collected']
['volunteers', 'consequence', 'these', 'datasets']
['unbalanced', 'labels', 'different', 'sources']
['42378', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets']
['figure', 'addition', 'positive', 'samples', 'covidcxr', 'chexpert', 'dataset', 'green', 'dotted']
['lines', 'correct', 'behavior', 'biased', 'dataset', 'background', 'included']
['dotted', 'lines', 'indicate', 'expected', 'reduction', 'classification', 'lungs', 'removed']
['analysis', 'lines', 'accuracy', 'given', 'expansion', 'reduction', 'vertical']
['indicating', 'standard', 'deviation']
['makes', 'getting', 'robust', 'model', 'reliable', 'performance']
['measures', 'difficult', 'regard', 'articles', 'report']
['problem', 'small', 'unbalanced', 'datasets', 'covid']
['detection', 'propose', 'solutions', 'mitigate']
['problem']
['analysis', 'tackled', 'other', 'authors']
['instance', 'authors', 'proposed', 'train']
['partitions', 'should', 'different', 'datasets', 'related']
['classifier', 'trying', 'achieve', 'maximum']
['performance', 'certain', 'dataset']
['assert', 'generalization', 'capacity', 'classi', 'other', 'sought', 'minimize', 'effects']
['different', 'biased', 'datasets', 'converting', 'different']
['dataset', 'observations', 'prototypes', 'greatly', 'reducing', 'possible']
['intra', 'dataset', 'specific', 'features']
['recently', 'addresses', 'issue', 'covid', 'detection']
['reports', 'problem', 'mixing', 'different', 'datasets']
['network', 'learn', 'background', 'information', 'study']
['performs', 'similar', 'approach', 'presented', 'thisar', 'ticle', 'study', 'possible', 'biases', 'within', 'lungs']
['occludes', 'lungs', 'rectangular', 'fixed', 'black', 'boxes']
['measures', 'accuracy', 'achieved', 'however', 'proposed']
['methodology', 'extends', 'concept', 'proposed', 'precise']
['masks', 'progressive', 'inclusion', 'exclusion', 'information']
['learning', 'process', 'allows', 'ability', 'detect']
['where', 'approximately', 'enables', 'precise']
['estimation']
['furthermore', 'studies', 'within', 'ncov2019', 'dataset']
['using', 'information', 'about', 'patients', 'symptoms', 'comorbidities']
['dataset', 'collects', 'clinical', 'differ', 'sources', 'rather', 'images', 'found', 'significant']
['related', 'origin', 'exposed', 'several', 'issues']
['related', 'multisource', 'variability']
['article', 'focused', 'detecting', 'biases', 'within']
['widely', 'datasets', 'glimpse', 'degree', 'which']
['these', 'biases', 'affect', 'results', 'proposes', 'detection']
['methodology', 'assert', 'validity', 'results', 'methodol', 'makes', 'techniques', 'heatmap', 'visualization']
['histogram', 'analysis', 'selective', 'image', 'occlusion', 'which']
['combined', 'evaluate', 'which', 'parts', 'images', 'being']
['discriminative', 'features', 'classification']
['methodology', 'applied', 'scenar', 'existence', 'individual', 'pneumonia']
['datasets', 'another', 'detect', 'existence']
['datasets']
['limitations', 'study']
['regarding', 'possible', 'limitations', 'there', 'could', 'problem']
['methodology', 'proposed', 'since', 'segmentation', 'masks']
['expansion', 'reduction', 'biased', 'themselves']
['segmentation', 'process', 'might', 'prone']
['images', 'pneumonia', 'since', 'borders', 'lungs']
['diffuse', 'whereas', 'could', 'happen', 'images']
['control', 'patients', 'could', 'significant', 'difference']
['volume', '42379o', 'catalá', 'analysis', 'public', 'image', 'datasets']
['figure', 'occlusion', 'fixed', 'rectangular', 'boxes']
['between', 'cases', 'controls', 'masks', 'therefore', 'might']
['introducing', 'would', 'imply', 'problem']
['proposed', 'methodology']
['however', 'designed', 'experiment', 'where']
['occlusion', 'masks', 'substituted', 'rectangles']
['lungs', 'experiment', 'similar', 'presented']
['ensure', 'lungs', 'completely']
['removed', 'using', 'segmentation', 'shape', 'whereas']
['aforementioned', 'place', 'fixed', 'black', 'angle', 'central', 'leaving', 'uncovered']
['examples', 'method', 'figure']
['results', 'achieved', 'bimcv', 'dataset']
['figure', 'where', 'differences', 'found', 'significant']
['suggesting', 'shape', 'masks', 'influencing']
['detection', 'algorithm', 'proposed']
['furthermore', 'increase', 'confidence', 'conclusions']
['processed', 'images', 'means', 'clahe', 'togram', 'normalization', 'assert', 'process', 'affected']
['results', 'figure', 'there', 'difference']
['results', 'achieved', 'between', 'normalized', 'plain']
['images']
['talking', 'about', 'strengths', 'results', 'experiments']
['described', 'section', 'demonstrated', 'classification']
['improve', 'background', 'included']
['images', 'which', 'means', 'either', 'there']
['specifically', 'background', 'significant']
['already', 'within', 'lungs', 'however']
['progressively', 'removed', 'image']
['experiments', 'accuracy', 'decrease', 'suggesting']
['figure', 'comparison', 'between', 'grain', 'squared', 'masks', 'bimcv', 'dataset']
['42380', 'volume', '2021o', 'catalá', 'analysis', 'public', 'image', 'datasets']
['figure', 'comparison', 'between', 'normalized', 'plain', 'bimcv', 'dataset']
['system', 'classifying', 'images', 'according']
['elements', 'present', 'whole', 'image', 'inside']
['lungs', 'result', 'confirms', 'hypothesis', 'powerful', 'convolutional', 'networks', 'subtle', 'features']
['images', 'optimistic', 'classification', 'results']
['measures', 'taken', 'avoid', 'biases']
['summarize', 'further', 'research', 'should', 'conducted']
['reduce', 'impact', 'intrinsic', 'datasets', 'whose']
['images', 'collected', 'several', 'sources', 'recent', 'literature']
['demonstrated', 'emergence', 'methodologies', 'useful']
['reduce', 'impact', 'image', 'preprocess', 'methods', 'learning', 'architectures', 'designed']
['biased', 'datasets', 'starting']
['point']
['conclusion']
['novel', 'methodology', 'assess', 'existence']
['image', 'datasets', 'presented', 'techniques']
['activation', 'heatmap', 'visualization', 'histogram', 'analysis']
['selective', 'image', 'occlusion', 'combined', 'evaluate', 'which']
['images', 'being', 'discriminative', 'features']
['classification', 'regions', 'interest']
['lungs', 'datasets', 'different', 'levels']
['these', 'comprising', 'datasets', 'informa', 'quickly', 'available', 'urgent', 'scenario', 'current']
['covid', 'crisis', 'examples', 'bimcv', 'collection']
['combination', 'datasets', 'created', 'purpose', 'which']
['problems', 'results', 'confirmed']
['other', 'methodologies']
['activation', 'histogram', 'analysis']
['study', 'effects', 'combining', 'datasets', 'differ', 'sources', 'especially', 'interesting', 'because', 'shows']
['strictly', 'controlled', 'important', 'biases', 'induced']
['final', 'dataset', 'typical', 'solution', 'samples']
['given', 'class', 'compile', 'different', 'datasets']
['collects', 'categories', 'study', 'recent', 'covid']
['datasets', 'particular', 'widely', 'covidcxr', 'dataset']
['built', 'different', 'sources', 'might', 'included']
['significant', 'biases', 'inadvertently', 'affected', 'results', 'lished', 'heterogeneous', 'dataset', 'often', 'observa', 'tions', 'coming', 'diverse', 'equipment', 'acquisition', 'proto', 'processing', 'software', 'context', 'features', 'found']
['convolutional', 'networks', 'images', 'including']
['background', 'areas', 'enough', 'classification']
['whilst', 'actual', 'performance', 'classifier', 'clinical']
['attempted', 'lower']
['acknowledgments']
['authors', 'would', 'thanks', 'gratitude', 'bimcv']
['other', 'teams', 'compiled', 'available']
['datasets', 'experiments', 'ducted', 'employing', 'instituto', 'tecnológico', 'informática']
['performance', 'computing', 'platform', 'which', 'funded']
['ivace', 'implemented', 'within']
['space', 'being', 'these', 'experiments', 'tech4cv', 'project']
[]
['volume', '42381o', 'catalá', 'analysis', 'public', 'image', 'datasets']

# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_science_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_science.show_topic(0, topn=5))

{'covid': 0.8173316479466738,
 'imag': 0.32835719547350367,
 'model': 0.19244766283134151,
 'detect': 0.1700824506894798,
 'dataset': 0.13468398563227243}

from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph25.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled28.png')

cosine-similarity
               covid         imag        model       detect      dataset
covid              1   0.00123781  0.000365384   0.00800962  0.000934745
imag      0.00123781            1  0.000764231  -0.00799469  -0.00232223
model    0.000365384  0.000764231            1   0.00280805 -0.000605737
detect    0.00800962  -0.00799469   0.00280805            1  0.000416076
dataset  0.000934745  -0.00232223 -0.000605737  0.000416076            1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='green',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig28.png')

covid-psychology-large.txt

document_list,titles,word_count_psy=load_data("","covid-psychology-15.txt")

Number of words in text file : 58236
Total Number of Documents: 6353

clean_text=preprocess_data(document_list)

['international', 'sociology']
[]
['author']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['0268580920948807']
['journals', 'sagepub']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'negatively', 'impacted', 'people', 'lives', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'considered', 'reviewed', 'article']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['modern', 'mainstream', 'social', 'media', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'challenging', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman']
['covid', 'pandemic', 'outbreak', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'world', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms']
['become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'caused', 'several', 'mishaps', 'across', 'world', 'instance']
['after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'overdosed']
['cholorquine', 'after', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', 'there', 'failure']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['science', 'conspiracy', 'theories', 'among', 'general', 'public', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'vaccine', 'covid']
['create', 'social', 'stigma', 'resulting', 'xenophobia', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'adverse', 'health']
['psychosocial', 'impacts', 'aguilera', '2020a', '2020b', 'these', 'considerations', 'become', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe']
['popular', 'opinion', 'spend', 'social', 'media', 'people', 'trying']
['sense', 'their', 'changed', 'lives', 'proclaimed', 'celebrities', 'identified', 'social']
['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their']
['subjective', 'interpretations', 'events', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer']
['scientific', 'studies', 'previous', 'disease', 'outbreaks', 'demonstrated', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', 'kalichman', 'individuals', 'endorse']
['misinformation', 'about', 'disease', 'likely', 'follow', 'public', 'health', 'instructions', 'during']
['ebola', 'outbreak', 'respondents', 'credence', 'conspiracy', 'theories']
['alleged', 'would', 'likely', 'support', 'during', 'disease', 'outbreak']
['national', 'cancer', 'institute', 'study', 'montanaro', 'conducted']
['respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information']
['about', 'covid', 'current', 'administration', 'mainstream', 'media', 'outlets']
['times']
['mukhtar']
['psychology', 'misinfodemics']
['adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual']
['perceived', 'threat', 'mertens', 'however', 'excessive']
['detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'anxiety']
['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia']
['insufficient', 'cause', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation']
['policies', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal']
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['violence', 'marital', 'child', 'abuse', 'domestic', 'violence', 'racism', 'xenophobia', 'dissociation']
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['mukhtar']
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['pandemic', 'creating', 'public', 'panic', 'social', 'unrest', 'distrust', 'systems']
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['people', 'isolation', 'social', 'distancing', 'quarantine', 'crisis', 'situation']
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['service', 'industries', 'financial', 'markets']
['moreover', 'coronavirus', 'outbreak', 'severely', 'affected']
['financial', 'markets', 'while', 'declining', 'value', 'stock', 'index']
['daube', 'surprisingly', 'specific', 'events']
['fluctuate', 'stock', 'values', 'shehzad', 'sohail', 'figure']
['revealed', 'market', 'value', 'european', 'american', 'chinese']
['markets', 'january', 'march']
['period', 'divided', 'three', 'rounds', 'comparison', 'purposes']
['first', 'lagged', 'january', 'january']
['second', 'lagged', 'january', 'march']
['third', 'lagged', 'denoted', 'period', 'march', 'march']
['results', 'showed', 'market', 'values', 'these']
['indices', 'significantly', 'declined', 'third', 'lagged', 'covid', 'approached', 'western', 'countries', 'spain', 'stock', 'market']
['share', 'value', 'decreased', 'third', 'lagged']
['covid', 'tremendously', 'shrank', 'greece', 'stock', 'markets']
['moreover', 'figure', 'illustrates', 'volatility', 'index']
['january', 'march', 'denotes', 'world', 'faced']
['severe', 'financial', 'crises', 'stock', 'markets', 'collapsed']
['economies', 'through', 'enormous', 'pressure', 'similar']
['scenario', 'built', 'during', 'global', 'pandemic', 'covid', 'stock', 'market', 'variance', 'highest', 'second']
['after', 'global', 'financial', 'crises']
['regard', 'novel', 'coronavirus', 'impact']
['detrimental', 'pandemic', 'behavior', 'unknown', 'effect']
['stock', 'return', 'longer', 'relevant']
['stock', 'volatility', 'agitated', 'enormous', 'spread']
['pandemic', 'resulting', 'severe', 'economic', 'crisis', 'sharif']
['circumstances', 'quite', 'significant']
['analyze', 'coronavirus', 'short', 'impacts']
['advanced', 'countries', 'stock', 'return', 'investigation', 'argued']
['confirmed', 'patients', 'novel', 'coronavirus', 'increase']
['lockdown', 'conditions', 'becomes', 'stricter', 'which']
['significant', 'economic', 'crisis', 'shocked', 'economic', 'stability']
['nations', 'moreover', 'deaths', 'befell', 'result', 'novel']
['coronavirus', 'specified', 'deficiency', 'health', 'facilities', 'which']
['caused', 'substantial', 'health', 'crisis', 'particularly', 'examination']
['analyzed', 'effects', 'economic', 'health', 'crisis']
['novel', 'coronavirus', 'behavior', 'financial', 'markets']
['fatality', 'while', 'fatality', 'novel', 'coronavirus']
['around', 'globe']
['united', 'states', 'germany', 'united', 'kingdom', 'italy']
['spain', 'period', 'february', 'april']
['study', 'elucidates', 'effects', 'executing', 'linear', 'autoregressive', 'distributed', 'lagged', 'nardl', 'model']
['investigation', 'provides', 'remarkable', 'policies', 'handle']
['impacts', 'covid', 'financial', 'market']
['answers', 'momentous', 'queries', 'researchers', 'policymakers']
['government', 'officials', 'academicians', 'firstly']
['covid', 'linear', 'impact', 'financial', 'markets']
['behavior', 'secondly', 'health', 'crisis', 'economic', 'instability']
['generated', 'covid', 'significant', 'impact']
['index', 'thirdly', 'nasdaq', 'composite', 'index']
['index', 'respond', 'critically', 'covid', 'crisis']
['fourthly', 'economic', 'crisis', 'generated', 'covid']
['health', 'crisis', 'significant', 'impact', 'stock', 'returns']
['fifthly', 'suspended', 'circle', 'economy']
['lockdown', 'begin', 'again', 'according', 'author', 'knowledge']
['first', 'study', 'examines', 'asymmetrical', 'impact']
['covid', 'psychology', 'stock', 'markets', 'mostly']
['infected', 'nations', 'world', 'firstly', 'project', 'ascertained']
['stationary', 'level', 'study', 'variables', 'discovered']
['variables', 'stationary', 'stationary']
['hence', 'linear', 'version', 'autoregressive']
['distributed', 'model', 'applied']
['methodology']
[]
['study', 'utilized', 'daily', 'number', 'confirmed']
['patients', 'deaths', 'covid', 'stock', 'markets']
['spain', 'italy', 'germany', 'period']
['february', 'april', 'study', 'taken']
['database', 'yahoo', 'finance', 'european', 'center']
['disease', 'control', 'prevention', 'further', 'investigation']
['analyzed', 'daily', 'returns']
['london', 'stock', 'exchange', 'nomination', 'spain', 'italy']
['germany', 'however', 'nasdaq', 'composite']
['index', 'represents']
['methodology']
['investigation', 'utilized', 'stock', 'market', 'daily', 'returns']
['dependent', 'variable', 'confirmed', 'cases', 'deaths']
['independent', 'variable', 'daily', 'returns', 'study']
['computed', 'follows', 'syllignakis', 'kouretas', 'shehzad']
['sohail']
[]
[]
[]
['denote', 'return', 'value', 'closing']
['price', 'previous', 'closing', 'price']
['stock', 'market', 'respectively', 'linear', 'association', 'between', 'these']
['variables', 'defined']
['1sprt', 'β1usct', 'β2usdt']
['1nsrt', 'β1usct', 'β2usdt']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'variations', 'global', 'financial', 'crisis']
['covid', 'crisis']
['1ibert', 'β1spct', 'β2spdt']
['1ftmrt', 'β1itlct', 'β2itldt']
['1daxrt', 'β1gerct', 'β2gerdt']
['1lsert', 'β1ukct', 'β2ukdt']
['where', 'signifies', 'first', 'difference', 'β0β1β2']
['independent', 'parameters', 'however']
['defines', 'return', 'values', 'nasdaq']
['composite', 'index']
['london', 'stock', 'exchange', 'respectively', 'moreover']
['indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively']
['further', 'refer', 'confirmed']
['deaths', 'covid', 'spain', 'italy', 'germany']
['respectively']
['ascertain', 'short', 'asymmetries']
['study', 'employed', 'linear', 'autoregressive', 'distributed']
['nardl', 'model', 'introduced', 'model']
['performs', 'small', 'number', 'observations']
['applied', 'mixed', 'level', 'stationary']
['evaluates', 'linearity', 'cointegration', 'between', 'variables']
['equation', 'shared', 'short', 'variations']
['asymmetries', 'after', 'taking', 'parameters', 'account']
['nardl', 'model', 'extended']
['pesaran', 'model', 'hence']
['nardl', 'model', 'stock', 'market', 'specified']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['γ1ftmrt', 'γ2aitlc']
[]
[]
[]
[]
[]
[]
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
[]
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'parameters']
['whereas', 'symbolizes', 'number', 'model']
['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
['criterion', 'above', 'stated', 'equations', 'assumed']
['confirmed', 'number', 'cases', 'deaths', 'covid']
['asymmetrical', 'impact', 'stock', 'returns']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['implies', 'negative', 'shock', 'variable']
['computed', 'follows']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['delineates', 'positive', 'shock', 'variable', 'denotes']
['asymmetric', 'distributive', 'error', 'correction']
['these', 'factors', 'written', 'follows']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['φ1ectt']
['directs', 'error', 'correction']
['cointegration', 'among', 'variables', 'examined', 'through', 'bound']
['approach', 'pesaran', 'method', 'relies', 'evaluate', 'hypothesis']
['purpose', 'pesaran', 'defined', 'bounds']
['upper', 'lower', 'bound', 'estimated', 'statistics']
['higher', 'upper', 'bound', 'limit', 'hypothesis']
['cointegration', 'rejected', 'however', 'projected', 'value', 'lower', 'lower', 'bound', 'limit', 'hypothesis', 'cannot']
['rejected', 'besides', 'value', 'remains', 'between']
['limits', 'results', 'conclusive', 'project']
['employed', 'verify', 'asymmetric']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['andshort']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['asymmetric']
['relationship', 'between', 'study', 'variables']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['study', 'findings']
['descriptive', 'summary', 'showed', 'table', 'during', 'covid', 'stock', 'returns', 'negative', 'further', 'skewness']
['values', 'these', 'markets', 'negative', 'except']
['kurtosis', 'which', 'predicts', 'chances', 'these']
['table', 'presents', 'results', 'augmented', 'dickey', 'fuller']
['dickey', 'fuller', 'phillip', 'perron']
['phillips', 'perron', 'showed', 'study', 'variables']
['diverse', 'stationary', 'levels', 'variable']
['cointegrated']
['bound', 'cointegration', 'fallouts']
['outcomes', 'bound', 'displayed', 'table', 'disclosed']
['significant', 'statistical', 'evidence', 'association', 'between']
['study', 'variables', 'equation', 'statistics', 'joint']
['significance', 'lagged', 'level', 'parameters', 'stated']
['respectively', 'these', 'values']
['surpass', 'upper', 'bound', 'limits', 'implying']
['cointegration', 'exists', 'among', 'economic', 'crisis', 'health']
['crisis', 'financial', 'markets', 'equation']
['short', 'asymmetric']
['cointegration', 'reckoning']
['table', 'exhibited', 'upshots', 'statistics']
['particularized', 'linear', 'association']
['however', 'revealed', 'short', 'asymmetric']
['affiliation', 'besides', 'caused', 'asymmetric']
['influence', 'short', 'periods']
['while', 'possessed', 'short', 'asymmetry', 'additionally']
['table', 'descriptive', 'statistics']
[]
['41737', '08997', '70833', '28187']
['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996']
['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965']
['jarque', '26244', '69524', '87966', '47298', '76708']
['probability', '00434', '000161']
[]
['18523', '54717', '42086', '40245']
['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595']
['kurtosis', '43888', '59924', '573906', '120135', '135106', '868864', '472667', '04434']
['jarque', '44518', '49843', '45175', '59309', '03773', '08892']
['probability', '000013', '003185', '000121', '000872']
['source', 'author', 'calculation']
['table']
['statistics', 'statistics']
['variable', 'level', 'level']
['893713', '54426', '057616']
['293963', '969976', '971366', '51473']
['935247', '359161', '147762', '393867']
['631659', '55646', '451458']
['127702', '174362', '400067', '35456']
['300231', '849862', '488626', '372171']
['27056', '985616', '446574', '65801']
['506727', '32241', '159422', '09496']
['552953', '028486', '374729', '97616']
['775255', '591517', '768878', '87539']
['816325', '578091', '746256', '61778']
['429147', '36282', '571441', '31254']
['89506', '022076', '13962']
['847753', '567572', '310638', '46537']
['138569', '097646', '384682', '006879']
['66594', '40744', '64235', '28961']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'results', 'bound']
['statistic', 'value', 'signif', 'statistics', 'value', 'signif']
[]
['statistic', '02410', 'statistic', '165881']
[]
[]
[]
['statistic', '11238', 'statistic', '23325']
[]
[]
[]
['statistic', '490181', 'statistic', '03259']
[]
[]
['source', 'author', 'calculation']
['table']
['asymmetry', 'short', 'asymmetry', 'asymmetry', 'short', 'asymmetry']
[]
['242605']
[]
[]
['574046']
['589403', '609162', '338933']
[]
['569179', '725822', '640751']
['61007', '786146', '718125']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['results', 'designated', 'asymmetric']
['relationship', 'statistics', 'enlightened']
['short', 'linear', 'impacts']
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['moreover', 'denotes', 'asymmetric', 'liaison']
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['choose', 'nardl', 'model', 'computing']
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['linear', 'fallouts', 'presented', 'table', 'illustrated']
['expansion', 'contraction', 'brings', 'reduction']
['increase', 'respectively', 'indicates', 'strict', 'lockdown']
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['terms', 'described', 'uptick', 'effect', 'improved']
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['owing', 'covid']
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['positive', 'negative', 'shocks', 'escalate']
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['increase', 'specifies', 'upsurge', 'health', 'crisis']
['expressively', 'distressed', 'nasdaq', 'composite', 'index']
['versa', 'nonetheless', 'short', 'negative', 'shock']
['demonstrated', 'imperative', 'decrease']
['whereas', 'expansion', 'lessens', 'negative']
['significant', 'parameter', 'stated', 'financial', 'instability']
['generated', 'covid', 'would', 'settled', 'speed']
['units', 'square', 'value', 'signified']
['instability', 'occurring', 'result', 'covid']
['finding', 'parameters', 'model', 'table', 'identified']
['assertive', 'shock', 'encouraging', 'effect']
['besides', 'adverse', 'shocks', 'remain', 'insignificant', 'other']
['escalation', 'decline', 'negative', 'positive', 'influence']
['implying', 'strict', 'lockdown', 'considerably', 'caused']
['financial', 'crisis', 'spain', 'period', 'short']
['period', 'intensification', 'decline']
['increased', 'negative', 'significant', 'value']
['elucidated', 'today', 'disequilibrium', 'adjust']
['speed', 'units', 'coefficient', 'square']
['stated', 'variations', 'owing', 'covid']
['table', 'displayed', 'fallouts', 'stock', 'market']
['condition', 'covid', 'italy', 'stock', 'markets', 'exposed']
['frontiers', 'psychology', 'frontiersin', 'september', 'volume', 'article', '1924fpsyg', '01924', 'september']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['usc_neg', '047867', '003539', '52394']
['usc_neg', '061323', '003402', '02596']
['usc_neg', '108895', '005569', '55339']
['usc_neg', '072915', '003619', '14618']
['usd_pos', '713848', '036514', '55011']
['usd_pos', '23431', '014268', '42276']
['usd_pos', '194744', '01369', '22562']
['usd_pos', '334891', '029691', '27907']
['usd_neg', '377433', '055371', '816426']
['usd_neg', '65514', '097958', '89641']
['usd_neg', '074692', '02737', '728986']
['usd_neg', '650475', '235321', '51275']
['369525', '066813', '49782']
['squared', '912909']
['adjusted', 'squared', '892007']
['durbin', 'watson', '006439']
['variables', 'coefficients', 'error']
['usc_pos', '004491', '001667', '694157']
['usc_neg', '054381', '014406', '774912']
['usd_pos', '064436', '029631', '174626']
['usd_neg', '290139', '338048', '858278']
['181416', '264851', '684971']
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['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['usd_neg', '039324', '019503', '016267']
['usc_pos', '002147', '000382', '622014']
['usc_neg', '002319', '00069', '360414']
['usc_neg', '008198', '001064', '703282']
['501702', '093855', '00026']
['squared', '812596']
['adjusted', 'squared', '800102']
['durbin', 'watson', '130245']
['variables', 'coefficients', 'error']
['usd_pos', '013042', '004465', '920922']
['usd_neg', '026413', '030567', '864107']
['usc_pos', '001013', '00034', '977381']
['usc_neg', '004258', '001923', '214416']
['525513', '274541', '914153']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['reduction', 'diminished', 'amplified']
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['enlarged', 'imperatively', 'condition']
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['harmed', 'returns', 'growth', 'negative']
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['caused', 'covid']
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['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'asymmetric', 'parameters']
['variables', 'short', 'coefficients', 'error']
['368944', '09522']
['spd_pos', '018471', '009268']
['spd_neg', '026216', '010675']
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['there', 'competition', 'exits', 'narrow', 'unfamiliar']
['people', 'compete', 'coordinate', 'evacuations', 'positioned']
['psychologically', 'individuals', 'rather', 'group', 'members', 'mintz', 'shows']
['evacuating', 'crowd', 'blocks', 'explained', 'terms']
['prevalence', 'individual', 'competition', 'collective', 'setting', 'rather', 'terms']
['excessive', 'emotion', 'these', 'cases', 'emergency', 'badly', 'absence']
['collective', 'psychology', 'coordination', 'cooperation']
['cooperating', 'giving', 'support', 'carry', 'risks', 'which', 'acknowledged']
['evacuations', 'larger', 'group', 'slower', 'egress', 'because', 'speed', 'reduced']
['through', 'people', 'interacting', 'other', 'aguirre', 'motivation']
['support', 'other', 'survivors', 'risks', 'their', 'personal', 'safety']
['collective', 'emergency', 'always', 'particular', 'individuals']
['drury', 'cocking', 'reicher', 'covid', 'individual']
['supporting', 'group', 'clear', 'where', 'supportive', 'behaviour', 'involves', 'physical']
['proximity', 'whether', 'delivering', 'giving', 'emotional', 'support']
['about', 'public', 'under', 'reaction', 'occur', 'under', 'estimation']
['sometimes', 'characterized', 'optimistic', 'kinsey', 'gwynne', 'kuligowski']
['kinateder', 'context', 'where', 'emergency', 'events']
['reasonable', 'assume', 'happen', 'assumption', 'reverse']
['emergency', 'events', 'become', 'common', 'example', 'after', 'spate']
['terrorist', 'attacks', 'london', 'hundreds', 'people', 'oxford', 'street', 'noise']
['turned', 'harmless', 'general', 'extent', 'which', 'information', 'concerning']
['threat', 'plausible', 'function', 'broad', 'social', 'context', 'dangers', 'expectations']
['danger', 'raised', 'readiness', 'other', 'action', 'greater', 'context']
['recent', 'incidents', 'relevant', 'social', 'group']
['perceptions', 'become', 'collective', 'people', 'respond', 'direct']
['signals', 'other', 'people', 'responses', 'signal', 'bruder', 'fischer', 'manstead']
['suggest', 'extent', 'which', 'response', 'others', 'possible', 'threat']
['conveying', 'information', 'dependent', 'relevance', 'these', 'others']
['particular', 'context', 'which', 'often', 'function', 'shared', 'identity', 'based']
['about', 'social', 'influence', 'processes', 'other', 'contexts', 'bruder']
['covid', 'plausible', 'suppose', 'sight', 'others', 'community', 'routinely']
['observing', 'ignoring', 'social', 'distancing', 'regulations', 'example', 'likely', 'strong']
['signal', 'around', 'safety', 'doing', 'particularly', 'where', 'identify']
['community', 'these', 'exemplars', 'prototypes']
['psychological', 'factors', 'interact', 'management', 'failures', 'explain']
['emergency', 'events', 'badly', 'fearing', 'public', 'panic', 'leads', 'authorities', 'withhold']
['information', 'about', 'emergency', 'drury', 'information']
['emergency', 'increases', 'public', 'anxiety', 'public', 'perceives', 'information']
['being', 'withheld', 'damages', 'their', 'relationship', 'authority', 'carter']
['consequently', 'authorities', 'release', 'correct', 'information']
['public', 'mistrust', 'covid', 'treat']
['public', 'respect', 'order', 'build', 'trust', 'advice', 'given', 'behavioural']
['scientists', 'government']
['discussion', 'conclusions']
['emergencies', 'people', 'behave', 'selfishly', 'thoughtlessly']
['react', 'indeed', 'explained', 'research', 'suggests', 'conditions']
['competition', 'prevail', 'cooperation', 'questioning', 'notion']
['public', 'reactions', 'default', 'major', 'cause', 'problems', 'covid']
['crisis', 'existing', 'literature', 'disasters', 'support', 'prima', 'facie']
['major', 'problems', 'covid', 'response', 'outcomes', 'better', 'understood']
['otherwise', 'terms', 'political', 'under', 'reaction', 'systemic', 'issues', 'mismanagement']
['collective', 'panic', 'referred', 'disaster', 'literature', 'disasters', 'drury']
['rather', 'neutral', 'description', 'people', 'actually', 'behave']
['understood', 'particular', 'discourse', 'cultural', 'representation', 'which']
['psychologizes', 'indeed', 'pathologizes', 'public', 'responses', 'emergencies', 'disasters']
['given', 'known', 'about', 'under', 'reaction', 'systemic', 'factors', 'mismanagement']
['emergencies', 'emphasize', 'instead', 'collective', 'behaviour', 'clear']
['ideological', 'functions', 'naturalizing', 'fatalities', 'distracts', 'causes']
['might', 'responsible', 'mismanagement', 'instead', 'blaming', 'victims']
['irony', 'course', 'being', 'problem', 'collective', 'psychology']
['emergencies', 'solidarity', 'cooperation', 'commonly', 'witnessed', 'among', 'community']
['members', 'strangers', 'usually', 'solution', 'collective', 'psychology', 'therefore']
['should', 'harnessed', 'effectively', 'policy', 'practice', 'covid', 'response']
['elcheroth', 'drury', 'through', 'framing', 'threat', 'solution', 'collective']
['terms', 'through', 'emphasizing', 'shared', 'norms', 'around', 'collective', 'being', 'safety']
['drury', 'people', 'emergencies', 'probably', 'because']
['collective', 'psychology']

# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values

#These variables are for further experimentation in vertical comparission mantel test
model_psy_paragraph=create_gensim_lsa_model(clean_text,number_of_topics,words)

dict(model_psy.show_topic(0, topn=5))

{'covid': 0.3665193827288808,
 'pandem': 0.36094244989669616,
 'health': 0.3248658297265896,
 'polici': 0.29428572192418073,
 'impact': 0.23191430022007598}

from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph26.png')

# LSA Model

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psy =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psy,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled29.png')

cosine-similarity
              covid       pandem       health       polici       impact
covid             1 -0.000251484  -0.00102935   0.00200395   0.00491427
pandem -0.000251484            1  -0.00206637 -0.000776059   0.00266831
health  -0.00102935  -0.00206637            1   0.00450039  0.000610171
polici   0.00200395 -0.000776059   0.00450039            1  -0.00567595
impact   0.00491427   0.00266831  0.000610171  -0.00567595            1

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *100  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig29.png')

mantel_test(5)

Pairwise-distance
               covid    pandem    health    polici    impact      imag  \
covid    2.10734e-08   1.29174   1.34614  0.996639  0.997331   1.40455   
pandem       1.29174         0   1.22761  0.831439  0.863993   1.30309   
health       1.34614   1.22761         0   0.89882  0.846913   1.34228   
polici      0.996639  0.831439   0.89882         0  0.130483  0.994958   
impact      0.997331  0.863993  0.846913  0.130483         0   1.00193   
imag         1.40455   1.30309   1.34228  0.994958   1.00193         0   
model        1.40535   1.30616   1.34301  0.994937   1.00125   1.40434   
detect       1.37031   1.27076   1.30048  0.953255   0.95357   1.38056   
dataset       1.4048   1.30695   1.34452  0.994289   1.00036   1.40636   

            model       detect   dataset  
covid     1.40535      1.37031    1.4048  
pandem    1.30616      1.27076   1.30695  
health    1.34301      1.30048   1.34452  
polici   0.994937     0.953255  0.994289  
impact    1.00125      0.95357   1.00036  
imag      1.40434      1.38056   1.40636  
model           0      1.37334   1.40534  
detect    1.37334  1.49012e-08   1.37484  
dataset   1.40534      1.37484         0  
Pairwise-distance
               covid    pandem    health       polici    impact       imag  \
covid    2.10734e-08   1.40413   1.39789      1.18336   1.39399   0.996695   
pandem       1.40413         0   1.39525       1.1809   1.39221   0.992328   
health       1.39789   1.39525         0      1.16999   1.38682   0.982234   
polici       1.18336    1.1809   1.16999  1.49012e-08   1.17576   0.647287   
impact       1.39399   1.39221   1.38682      1.17576         0   0.982498   
imag        0.996695  0.992328  0.982234     0.647287  0.982498          0   
model        1.04603   1.03991   1.02912     0.786791   1.03451   0.342652   
detect      0.995885  0.990851  0.981511     0.642914  0.981984  0.0495758   
dataset     0.995199   0.99045  0.980643     0.641984   0.98134  0.0456046   

               model       detect    dataset  
covid        1.04603     0.995885   0.995199  
pandem       1.03991     0.990851    0.99045  
health       1.02912     0.981511   0.980643  
polici      0.786791     0.642914   0.641984  
impact       1.03451     0.981984    0.98134  
imag        0.342652    0.0495758  0.0456046  
model    9.12506e-09     0.338194   0.338994  
detect      0.338194  4.65661e-10  0.0309682  
dataset     0.338994    0.0309682          0  

(-0.36722959799295146, 0.8156, -1.0916996508045393)

mantel_test(10)

Pairwise-distance
               covid    pandem    health     polici    impact      countri  \
covid    2.10734e-08   1.29174   1.34614   0.996639  0.997331       1.0006   
pandem       1.29174         0   1.22761   0.831439  0.863993       0.8195   
health       1.34614   1.22761         0    0.89882  0.846913     0.909924   
polici      0.996639  0.831439   0.89882          0  0.130483    0.0886125   
impact      0.997331  0.863993  0.846913   0.130483         0     0.158511   
countri       1.0006    0.8195  0.909924  0.0886125  0.158511  2.63418e-09   
develop      1.01972  0.836573  0.945239   0.219142  0.258953     0.198135   
nation       1.05631  0.956099  0.890934   0.351072  0.338373     0.367674   
sustain     0.994409  0.845449  0.903259   0.042095  0.121871    0.0918988   
economi     0.995097  0.834319  0.891971  0.0466139  0.115609    0.0887288   
imag         1.40455   1.30309   1.34228   0.994958   1.00193      1.00063   
model        1.40535   1.30616   1.34301   0.994937   1.00125     0.998605   
detect       1.37031   1.27076   1.30048   0.953255   0.95357     0.953814   
dataset       1.4048   1.30695   1.34452   0.994289   1.00036     0.999229   
learn        1.39805   1.29449   1.33085   0.981304  0.971631     0.989446   
use          1.40326    1.3016   1.33906   0.988543   1.00225     0.997627   
class        1.40046   1.30392   1.33892   0.989182  0.993974     0.990393   
base         1.39188    1.2854   1.32855   0.975671  0.985234     0.979056   
chest        1.35157   1.22446   1.27388   0.907043  0.910788     0.891392   

          develop    nation    sustain    economi      imag     model  \
covid     1.01972   1.05631   0.994409   0.995097   1.40455   1.40535   
pandem   0.836573  0.956099   0.845449   0.834319   1.30309   1.30616   
health   0.945239  0.890934   0.903259   0.891971   1.34228   1.34301   
polici   0.219142  0.351072   0.042095  0.0466139  0.994958  0.994937   
impact   0.258953  0.338373   0.121871   0.115609   1.00193   1.00125   
countri  0.198135  0.367674  0.0918988  0.0887288   1.00063  0.998605   
develop         0   0.41009   0.218511   0.220412   1.02046    1.0164   
nation    0.41009         0   0.349667   0.334864   1.05385   1.05152   
sustain  0.218511  0.349667          0  0.0313482  0.993415  0.993578   
economi  0.220412  0.334864  0.0313482          0  0.994138  0.994042   
imag      1.02046   1.05385   0.993415   0.994138         0   1.40434   
model      1.0164   1.05152   0.993578   0.994042   1.40434         0   
detect   0.968051    1.0135   0.950806   0.946972   1.38056   1.37334   
dataset   1.01545   1.04781   0.993686   0.993929   1.40636   1.40534   
learn      1.0095   1.05018   0.981862   0.983133   1.39648   1.39551   
use       1.01089   1.05667   0.990938   0.991925   1.40281   1.40311   
class     1.01348   1.04952   0.987859   0.988145   1.40137   1.40122   
base     0.988448   1.02696   0.973888    0.97458   1.39307   1.39184   
chest    0.883996   0.97164   0.904441   0.903399   1.33931   1.34455   

              detect   dataset     learn          use     class      base  \
covid        1.37031    1.4048   1.39805      1.40326   1.40046   1.39188   
pandem       1.27076   1.30695   1.29449       1.3016   1.30392    1.2854   
health       1.30048   1.34452   1.33085      1.33906   1.33892   1.32855   
polici      0.953255  0.994289  0.981304     0.988543  0.989182  0.975671   
impact       0.95357   1.00036  0.971631      1.00225  0.993974  0.985234   
countri     0.953814  0.999229  0.989446     0.997627  0.990393  0.979056   
develop     0.968051   1.01545    1.0095      1.01089   1.01348  0.988448   
nation        1.0135   1.04781   1.05018      1.05667   1.04952   1.02696   
sustain     0.950806  0.993686  0.981862     0.990938  0.987859  0.973888   
economi     0.946972  0.993929  0.983133     0.991925  0.988145   0.97458   
imag         1.38056   1.40636   1.39648      1.40281   1.40137   1.39307   
model        1.37334   1.40534   1.39551      1.40311   1.40122   1.39184   
detect   1.49012e-08   1.37484   1.37019       1.3719   1.37034    1.3651   
dataset      1.37484         0   1.39613      1.40298   1.40176   1.39208   
learn        1.37019   1.39613         0      1.39579   1.39156   1.38103   
use           1.3719   1.40298   1.39579  1.49012e-08   1.40056   1.39178   
class        1.37034   1.40176   1.39156      1.40056         0   1.39185   
base          1.3651   1.39208   1.38103      1.39178   1.39185         0   
chest        1.31912   1.33768   1.32938      1.33524   1.33583   1.32553   

            chest  
covid     1.35157  
pandem    1.22446  
health    1.27388  
polici   0.907043  
impact   0.910788  
countri  0.891392  
develop  0.883996  
nation    0.97164  
sustain  0.904441  
economi  0.903399  
imag      1.33931  
model     1.34455  
detect    1.31912  
dataset   1.33768  
learn     1.32938  
use       1.33524  
class     1.33583  
base      1.32553  
chest           0  
Pairwise-distance
               covid    pandem    health       polici    impact   countri  \
covid    2.10734e-08   1.40413   1.39789      1.18336   1.39399   1.39432   
pandem       1.40413         0   1.39525       1.1809   1.39221   1.39094   
health       1.39789   1.39525         0      1.16999   1.38682   1.38454   
polici       1.18336    1.1809   1.16999  1.49012e-08   1.17576   1.17537   
impact       1.39399   1.39221   1.38682      1.17576         0   1.38651   
countri      1.39432   1.39094   1.38454      1.17537   1.38651         0   
develop      1.38489   1.38515   1.37755      1.16731   1.37591   1.36555   
nation       1.21755   1.21857   1.21016     0.823131   1.21174   1.21769   
sustain      1.36573   1.35662   1.34834      1.14091   1.35234   1.35677   
economi      1.37203   1.36941   1.36029      1.15818   1.35861   1.36218   
imag        0.996695  0.992328  0.982234     0.647287  0.982498  0.977732   
model        1.04603   1.03991   1.02912     0.786791   1.03451   1.03679   
detect      0.995885  0.990851  0.981511     0.642914  0.981984  0.977962   
dataset     0.995199   0.99045  0.980643     0.641984   0.98134  0.977426   
learn        1.00215   1.00095  0.998503     0.623397   1.00042  0.993506   
use          1.02389   1.02576   1.01012     0.675423   1.01065   1.01282   
class       0.996184   0.99145  0.981399     0.642767  0.982192  0.979189   
base         1.32217    1.3162   1.31425      1.10065   1.31409    1.3065   
chest              0         0         0            0         0         0   

          develop    nation      sustain      economi       imag        model  \
covid     1.38489   1.21755      1.36573      1.37203   0.996695      1.04603   
pandem    1.38515   1.21857      1.35662      1.36941   0.992328      1.03991   
health    1.37755   1.21016      1.34834      1.36029   0.982234      1.02912   
polici    1.16731  0.823131      1.14091      1.15818   0.647287     0.786791   
impact    1.37591   1.21174      1.35234      1.35861   0.982498      1.03451   
countri   1.36555   1.21769      1.35677      1.36218   0.977732      1.03679   
develop         0   1.13752      1.32203      1.34497   0.965994      1.00406   
nation    1.13752         0      1.17528      1.23061    0.69882     0.754526   
sustain   1.32203   1.17528  2.10734e-08      1.33354   0.935113      1.00733   
economi   1.34497   1.23061      1.33354  1.49012e-08   0.942429     0.995113   
imag     0.965994   0.69882     0.935113     0.942429          0     0.342652   
model     1.00406  0.754526      1.00733     0.995113   0.342652  9.12506e-09   
detect   0.963716  0.705588     0.934026     0.943407  0.0495758     0.338194   
dataset  0.963882  0.702796     0.933404     0.943422  0.0456046     0.338994   
learn    0.974276  0.751772     0.960399     0.958062   0.166589     0.371646   
use        1.0021  0.795146     0.942551     0.970375    0.25469     0.427486   
class    0.966376  0.702752     0.934681      0.94393  0.0609852     0.342388   
base      1.30421   1.12283       1.2633      1.28301   0.862478     0.835972   
chest           0         0            0            0          0            0   

              detect    dataset     learn          use      class      base  \
covid       0.995885   0.995199   1.00215      1.02389   0.996184   1.32217   
pandem      0.990851    0.99045   1.00095      1.02576    0.99145    1.3162   
health      0.981511   0.980643  0.998503      1.01012   0.981399   1.31425   
polici      0.642914   0.641984  0.623397     0.675423   0.642767   1.10065   
impact      0.981984    0.98134   1.00042      1.01065   0.982192   1.31409   
countri     0.977962   0.977426  0.993506      1.01282   0.979189    1.3065   
develop     0.963716   0.963882  0.974276       1.0021   0.966376   1.30421   
nation      0.705588   0.702796  0.751772     0.795146   0.702752   1.12283   
sustain     0.934026   0.933404  0.960399     0.942551   0.934681    1.2633   
economi     0.943407   0.943422  0.958062     0.970375    0.94393   1.28301   
imag       0.0495758  0.0456046  0.166589      0.25469  0.0609852  0.862478   
model       0.338194   0.338994  0.371646     0.427486   0.342388  0.835972   
detect   4.65661e-10  0.0309682   0.15534     0.249335  0.0473592  0.852356   
dataset    0.0309682          0  0.153312     0.247878  0.0345772  0.867173   
learn        0.15534   0.153312         0     0.262548   0.155206  0.873058   
use         0.249335   0.247878  0.262548  5.26836e-09   0.241858  0.928796   
class      0.0473592  0.0345772  0.155206     0.241858          0  0.868939   
base        0.852356   0.867173  0.873058     0.928796   0.868939         0   
chest              0          0         0            0          0         0   

        chest  
covid       0  
pandem      0  
health      0  
polici      0  
impact      0  
countri     0  
develop     0  
nation      0  
sustain     0  
economi     0  
imag        0  
model       0  
detect      0  
dataset     0  
learn       0  
use         0  
class       0  
base        0  
chest       0  

(-0.4775879754844936, 0.9934, -2.174520016647468)

def mantel_test_verticle(model_a,model_b,n):
    words_science = dict(model_a.show_topic(0, topn=word_count_science))
    words_psy = dict(model_b.show_topic(0, topn=word_count_psy))

    top_words_science = dict(model_a.show_topic(0, topn=n)) 
    top_words_psy = dict(model_b.show_topic(0,topn=n)) 


    c={} #law words in non law
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)

    b={} #non law words in law
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_a.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace for non-law
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_b.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=10000, method='pearson', tail='upper')

mantel_test_verticle(model_science_articles,model_science_p,5)

Pairwise-distance
           support    granada    covid     programm     research     imag  \
support          0   0.168942  1.53014     0.161791      0.26492  1.91808   
granada   0.168942          0  1.59104    0.0865721     0.263193  1.90158   
covid      1.53014    1.59104        0      1.60946      1.53741  2.33945   
programm  0.161791  0.0865721  1.60946  8.50177e-10     0.236021  1.89246   
research   0.26492   0.263193  1.53741     0.236021  4.80933e-09  1.88855   
imag       1.91808    1.90158  2.33945      1.89246      1.88855        0   
model      1.80501    1.77285  2.16792      1.76767      1.78488   2.7422   
dataset    1.52842    1.52151   2.3276      1.53273      1.53878  2.01837   
class       1.1637     1.1496  1.44823       1.1976      1.20895  2.18102   

                model  dataset    class  
support       1.80501  1.52842   1.1637  
granada       1.77285  1.52151   1.1496  
covid         2.16792   2.3276  1.44823  
programm      1.76767  1.53273   1.1976  
research      1.78488  1.53878  1.20895  
imag           2.7422  2.01837  2.18102  
model     2.72057e-08  2.37406  2.13815  
dataset       2.37406        0  1.87763  
class         2.13815  1.87763        0  
Pairwise-distance
           support  granada    covid  programm  research     imag    model  \
support          0  1.18984  4.44755  0.941912  0.941912  3.56952  2.99493   
granada    1.18984        0  3.34914   0.54807   0.54807  2.37968  1.88382   
covid      4.44755  3.34914        0   3.85527   3.85527  1.60718    2.676   
programm  0.941912  0.54807  3.85527         0         0  2.76062  2.07496   
research  0.941912  0.54807  3.85527         0         0  2.76062  2.07496   
imag       3.56952  2.37968  1.60718   2.76062   2.76062        0  1.09614   
model      2.99493  1.88382    2.676   2.07496   2.07496  1.09614        0   
dataset    2.99493  1.88382    2.676   2.07496   2.07496  1.09614        0   
class            0        0        0         0         0        0        0   

              dataset class  
support       2.99493     0  
granada       1.88382     0  
covid           2.676     0  
programm      2.07496     0  
research      2.07496     0  
imag          1.09614     0  
model               0     0  
dataset   9.31323e-09     0  
class               0     0  

(0.27763795369161115, 0.1765, 1.062241885835958)

mantel_test_verticle(model_science_articles,model_science_paragraph,5)

Pairwise-distance
           covid     imag        model   detect  dataset    class
covid          0  2.33945      2.16792  1.61978   2.3276  1.44823
imag     2.33945        0       2.7422  1.75983  2.01837  2.18102
model    2.16792   2.7422  2.72057e-08  1.68726  2.37406  2.13815
detect   1.61978  1.75983      1.68726        0  1.79884  1.49212
dataset   2.3276  2.01837      2.37406  1.79884        0  1.87763
class    1.44823  2.18102      2.13815  1.49212  1.87763        0
Pairwise-distance
           covid         imag    model   detect      dataset        class
covid          0      1.40496  1.40555   1.3692       1.4051      1.40077
imag     1.40496  2.10734e-08  1.40438  1.37789      1.40624      1.40121
model    1.40555      1.40438        0  1.37233      1.40535      1.40127
detect    1.3692      1.37789  1.37233        0      1.37291       1.3683
dataset   1.4051      1.40624  1.40535  1.37291  2.58096e-08      1.40167
class    1.40077      1.40121  1.40127   1.3683      1.40167  1.49012e-08

(0.6991566688423639, 0.018055555555555554, 1.783108826535045)

mantel_test_verticle(model_science_p,model_science_paragraph,5)

Pairwise-distance
            covid     imag    model   detect      dataset   support  granada  \
covid           0  1.60718    2.676    2.676        2.676   4.44755  3.34914   
imag      1.60718        0  1.09614  1.09614      1.09614   3.56952  2.37968   
model       2.676  1.09614        0        0            0   2.99493  1.88382   
detect      2.676  1.09614        0        0            0   2.99493  1.88382   
dataset     2.676  1.09614        0        0  9.31323e-09   2.99493  1.88382   
support   4.44755  3.56952  2.99493  2.99493      2.99493         0  1.18984   
granada   3.34914  2.37968  1.88382  1.88382      1.88382   1.18984        0   
programm  3.85527  2.76062  2.07496  2.07496      2.07496  0.941912  0.54807   
research  3.85527  2.76062  2.07496  2.07496      2.07496  0.941912  0.54807   

          programm  research  
covid      3.85527   3.85527  
imag       2.76062   2.76062  
model      2.07496   2.07496  
detect     2.07496   2.07496  
dataset    2.07496   2.07496  
support   0.941912  0.941912  
granada    0.54807   0.54807  
programm         0         0  
research         0         0  
Pairwise-distance
             covid         imag     model    detect      dataset   support  \
covid            0      1.40496   1.40555    1.3692       1.4051   1.03992   
imag       1.40496  2.10734e-08   1.40438   1.37789      1.40624   1.04488   
model      1.40555      1.40438         0   1.37233      1.40535   1.04005   
detect      1.3692      1.37789   1.37233         0      1.37291   1.01011   
dataset     1.4051      1.40624   1.40535   1.37291  2.58096e-08   1.04274   
support    1.03992      1.04488   1.04005   1.01011      1.04274         0   
granada   0.995027     0.993812  0.994144  0.948831     0.994089  0.310449   
programm  0.994901     0.993977  0.994012  0.949953     0.993727  0.287824   
research   1.02313      1.01966   1.02601  0.962638      1.02547  0.328554   

              granada  programm     research  
covid        0.995027  0.994901      1.02313  
imag         0.993812  0.993977      1.01966  
model        0.994144  0.994012      1.02601  
detect       0.948831  0.949953     0.962638  
dataset      0.994089  0.993727      1.02547  
support      0.310449  0.287824     0.328554  
granada   4.65661e-10  0.032859     0.240846  
programm     0.032859         0     0.233028  
research     0.240846  0.233028  7.45058e-09  

(0.22988121993944508, 0.1795, 1.0299334657787396)

mantel_test_verticle(model_psy_articles,model_psy_p,5)

Pairwise-distance
             misinfodem    covid    articl   health    mechan psycholog  \
misinfodem  1.36028e-08  1.59423  0.902727  1.69295  0.492125   1.57397   
covid           1.59423        0   1.46013  2.06287   1.47337   1.71712   
articl         0.902727  1.46013         0   1.5335  0.550356   1.23458   
health          1.69295  2.06287    1.5335        0    1.6193   1.66727   
mechan         0.492125  1.47337  0.550356   1.6193         0   1.33457   
psycholog       1.57397  1.71712   1.23458  1.66727   1.33457         0   
group            2.1644  2.45244   1.96966  2.61359   2.01066   2.36585   
pandem          1.12183  1.32272  0.770845  1.45061  0.949488   1.29309   

              group    pandem  
misinfodem   2.1644   1.12183  
covid       2.45244   1.32272  
articl      1.96966  0.770845  
health      2.61359   1.45061  
mechan      2.01066  0.949488  
psycholog   2.36585   1.29309  
group             0   2.24513  
pandem      2.24513         0  
Pairwise-distance
           misinfodem    covid    articl   health   mechan psycholog group  \
misinfodem          0  1.35287   2.09875  2.98805  2.98805   2.58051     0   
covid         1.35287        0   1.13552  1.67187  1.67187   1.70328     0   
articl        2.09875  1.13552         0  1.22885  1.22885  0.567759     0   
health        2.98805  1.67187   1.22885        0        0   1.35447     0   
mechan        2.98805  1.67187   1.22885        0        0   1.35447     0   
psycholog     2.58051  1.70328  0.567759  1.35447  1.35447         0     0   
group               0        0         0        0        0         0     0   
pandem        4.05861  2.70574    2.4577  1.22885  1.22885   2.52328     0   

                 pandem  
misinfodem      4.05861  
covid           2.70574  
articl           2.4577  
health          1.22885  
mechan          1.22885  
psycholog       2.52328  
group                 0  
pandem      2.63418e-08  

(-0.6355652933311675, 0.9678, -2.007492272513091)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,5)

Pairwise-distance
             covid    pandem   health    polici       impact psycholog  \
covid            0   1.32272  2.06287   1.47845      1.43686   1.71712   
pandem     1.32272         0  1.45061  0.840065     0.875398   1.29309   
health     2.06287   1.45061        0    1.6505      1.70716   1.66727   
polici     1.47845  0.840065   1.6505         0     0.609937   1.20742   
impact     1.43686  0.875398  1.70716  0.609937  1.36028e-08   1.38994   
psycholog  1.71712   1.29309  1.66727   1.20742      1.38994         0   
group      2.45244   2.24513  2.61359   2.05155      2.18441   2.36585   

             group  
covid      2.45244  
pandem     2.24513  
health     2.61359  
polici     2.05155  
impact     2.18441  
psycholog  2.36585  
group            0  
Pairwise-distance
             covid   pandem       health   polici   impact    psycholog  \
covid            0  1.40424      1.39832  1.18101   1.3943      1.40124   
pandem     1.40424        0      1.39659  1.17943  1.39343      1.39922   
health     1.39832  1.39659  2.98023e-08  1.16945  1.38795      1.39094   
polici     1.18101  1.17943      1.16945        0  1.17588      1.17297   
impact      1.3943  1.39343      1.38795  1.17588        0      1.39029   
psycholog  1.40124  1.39922      1.39094  1.17297  1.39029  2.58096e-08   
group      1.39464  1.39078      1.38352  1.16873   1.3845      1.38857   

             group  
covid      1.39464  
pandem     1.39078  
health     1.38352  
polici     1.16873  
impact      1.3845  
psycholog  1.38857  
group            0  

(0.37749281960674924, 0.173015873015873, 0.9466163475412356)

mantel_test_verticle(model_psy_p,model_psy_paragraph,5)

Pairwise-distance
              covid       pandem   health polici   impact misinfodem   articl  \
covid             0      2.70574  1.67187      0    2.236    1.35287  1.13552   
pandem      2.70574  2.63418e-08  1.22885      0  1.89336    4.05861   2.4577   
health      1.67187      1.22885        0      0   1.0317    2.98805  1.22885   
polici            0            0        0      0        0          0        0   
impact        2.236      1.89336   1.0317      0        0    3.34632  1.25066   
misinfodem  1.35287      4.05861  2.98805      0  3.34632          0  2.09875   
articl      1.13552       2.4577  1.22885      0  1.25066    2.09875        0   
mechan      1.67187      1.22885        0      0   1.0317    2.98805  1.22885   

             mechan  
covid       1.67187  
pandem      1.22885  
health            0  
polici            0  
impact       1.0317  
misinfodem  2.98805  
articl      1.22885  
mechan            0  
Pairwise-distance
              covid    pandem       health    polici    impact   misinfodem  \
covid             0   1.40424      1.39832   1.18101    1.3943      1.01939   
pandem      1.40424         0      1.39659   1.17943   1.39343      1.01529   
health      1.39832   1.39659  2.98023e-08   1.16945   1.38795      1.02419   
polici      1.18101   1.17943      1.16945         0   1.17588     0.688077   
impact       1.3943   1.39343      1.38795   1.17588         0      1.01745   
misinfodem  1.01939   1.01529      1.02419  0.688077   1.01745  7.45058e-09   
articl       1.3135   1.30856      1.30736   1.07248   1.30274      0.87454   
mechan      0.99498  0.993759     0.985503  0.638435  0.985301     0.216625   

              articl    mechan  
covid         1.3135   0.99498  
pandem       1.30856  0.993759  
health       1.30736  0.985503  
polici       1.07248  0.638435  
impact       1.30274  0.985301  
misinfodem   0.87454  0.216625  
articl             0  0.862229  
mechan      0.862229         0  

(-0.021563880670777167, 0.4867, -0.06129678024705728)

mantel_test_verticle(model_science_articles,model_science_p,10)

Pairwise-distance
           support    granada    covid     research     programm        tabik  \
support          0   0.168942  1.53014      0.26492     0.161791     0.200379   
granada   0.168942          0  1.59104     0.263193    0.0865721    0.0649291   
covid      1.53014    1.59104        0      1.53741      1.60946       1.5802   
research   0.26492   0.263193  1.53741  4.80933e-09     0.236021     0.298799   
programm  0.161791  0.0865721  1.60946     0.236021  8.50177e-10     0.151501   
tabik     0.200379  0.0649291   1.5802     0.298799     0.151501  3.40071e-09   
garcía    0.158953  0.0507223  1.59651     0.244374    0.0407668     0.114423   
decsai    0.159255  0.0649291  1.60444     0.240191     0.021643     0.129858   
project   0.279033   0.282792  1.62273     0.307197     0.229627     0.332025   
gómez     0.159282  0.0717116  1.60134     0.238796    0.0220085     0.135916   
imag       1.91808    1.90158  2.33945      1.88855      1.89246      1.91096   
model      1.80501    1.77285  2.16792      1.78488      1.76767      1.77949   
dataset    1.52842    1.52151   2.3276      1.53878      1.53273      1.51628   
class       1.1637     1.1496  1.44823      1.20895       1.1976      1.11666   
perform   0.924888   0.969531  1.68452     0.962291     0.934697     0.999789   
learn      1.35236    1.36047  1.89994      1.34907      1.34725      1.37388   
detect    0.912255   0.896688  1.61978     0.834118     0.857448     0.930328   
use        0.46925   0.601258  1.37432     0.629794     0.583067       0.6225   
base      0.557941   0.547801  1.30498     0.563681     0.540503     0.562031   

             garcía     decsai   project      gómez     imag        model  \
support    0.158953   0.159255  0.279033   0.159282  1.91808      1.80501   
granada   0.0507223  0.0649291  0.282792  0.0717116  1.90158      1.77285   
covid       1.59651    1.60444   1.62273    1.60134  2.33945      2.16792   
research   0.244374   0.240191  0.307197   0.238796  1.88855      1.78488   
programm  0.0407668   0.021643  0.229627  0.0220085  1.89246      1.76767   
tabik      0.114423   0.129858  0.332025   0.135916  1.91096      1.77949   
garcía            0  0.0220085   0.24045   0.021643  1.89376      1.77194   
decsai    0.0220085          0   0.24111  0.0156096  1.89438      1.76857   
project     0.24045    0.24111         0    0.22738  1.84183      1.82021   
gómez      0.021643  0.0156096   0.22738          0  1.89166      1.77085   
imag        1.89376    1.89438   1.84183    1.89166        0       2.7422   
model       1.77194    1.76857   1.82021    1.77085   2.7422  2.72057e-08   
dataset     1.53087    1.52947    1.5324     1.5339  2.01837      2.37406   
class       1.16594    1.18519   1.15644    1.17825  2.18102      2.13815   
perform    0.950934   0.942781  0.946773   0.942544  2.09899      1.42377   
learn       1.35218    1.35005   1.34388    1.34912  2.33698       2.1051   
detect     0.872296   0.866614  0.832302    0.86278  1.75983      1.68726   
use        0.591115    0.58647  0.622878   0.587135  1.89132      1.88147   
base        0.53881   0.541039  0.569539   0.537612  1.86736       1.7136   

          dataset    class      perform    learn    detect       use  \
support   1.52842   1.1637     0.924888  1.35236  0.912255   0.46925   
granada   1.52151   1.1496     0.969531  1.36047  0.896688  0.601258   
covid      2.3276  1.44823      1.68452  1.89994   1.61978   1.37432   
research  1.53878  1.20895     0.962291  1.34907  0.834118  0.629794   
programm  1.53273   1.1976     0.934697  1.34725  0.857448  0.583067   
tabik     1.51628  1.11666     0.999789  1.37388  0.930328    0.6225   
garcía    1.53087  1.16594     0.950934  1.35218  0.872296  0.591115   
decsai    1.52947  1.18519     0.942781  1.35005  0.866614   0.58647   
project    1.5324  1.15644     0.946773  1.34388  0.832302  0.622878   
gómez      1.5339  1.17825     0.942544  1.34912   0.86278  0.587135   
imag      2.01837  2.18102      2.09899  2.33698   1.75983   1.89132   
model     2.37406  2.13815      1.42377   2.1051   1.68726   1.88147   
dataset         0  1.87763       1.7877  2.01687   1.79884   1.58888   
class     1.87763        0      1.61368    1.785   1.49212   1.34592   
perform    1.7877  1.61368  1.92373e-08  1.49132   1.10838  0.963038   
learn     2.01687    1.785      1.49132        0    1.6455   1.36097   
detect    1.79884  1.49212      1.10838   1.6455         0  0.984045   
use       1.58888  1.34592     0.963038  1.36097  0.984045         0   
base      1.67631   1.2082      1.00457  1.40083  0.705965  0.644773   

                 base  
support      0.557941  
granada      0.547801  
covid         1.30498  
research     0.563681  
programm     0.540503  
tabik        0.562031  
garcía        0.53881  
decsai       0.541039  
project      0.569539  
gómez        0.537612  
imag          1.86736  
model          1.7136  
dataset       1.67631  
class          1.2082  
perform       1.00457  
learn         1.40083  
detect       0.705965  
use          0.644773  
base      9.61866e-09  
Pairwise-distance
           support   granada    covid  research  programm     tabik    garcía  \
support          0   1.18984  4.44755  0.941912  0.941912   1.62443   2.37968   
granada    1.18984         0  3.34914   0.54807   0.54807  0.470956   1.18984   
covid      4.44755   3.34914        0   3.85527   3.85527   3.08827   2.34183   
research  0.941912   0.54807  3.85527         0         0  0.797652    1.5953   
programm  0.941912   0.54807  3.85527         0         0  0.797652    1.5953   
tabik      1.62443  0.470956  3.08827  0.797652  0.797652         0  0.797652   
garcía     2.37968   1.18984  2.34183    1.5953    1.5953  0.797652         0   
decsai     1.41287  0.638836  3.61521  0.470956  0.470956   0.54807   1.28509   
project    1.41287  0.638836  3.61521  0.470956  0.470956   0.54807   1.28509   
gómez      2.07496  0.941912  2.88126   1.18984   1.18984  0.470956   0.54807   
imag       3.56952   2.37968  1.60718   2.76062   2.76062   1.97031   1.18984   
model      2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   
dataset    2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   
class            0         0        0         0         0         0         0   
perform          0         0        0         0         0         0         0   
learn      2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   
detect     2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   
use        2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   
base       2.99493   1.88382    2.676   2.07496   2.07496   1.41287  0.979511   

            decsai   project     gómez     imag     model      dataset class  \
support    1.41287   1.41287   2.07496  3.56952   2.99493      2.99493     0   
granada   0.638836  0.638836  0.941912  2.37968   1.88382      1.88382     0   
covid      3.61521   3.61521   2.88126  1.60718     2.676        2.676     0   
research  0.470956  0.470956   1.18984  2.76062   2.07496      2.07496     0   
programm  0.470956  0.470956   1.18984  2.76062   2.07496      2.07496     0   
tabik      0.54807   0.54807  0.470956  1.97031   1.41287      1.41287     0   
garcía     1.28509   1.28509   0.54807  1.18984  0.979511     0.979511     0   
decsai           0         0  0.797652  2.39296   1.62443      1.62443     0   
project          0         0  0.797652  2.39296   1.62443      1.62443     0   
gómez     0.797652  0.797652         0   1.5953  0.941912     0.941912     0   
imag       2.39296   2.39296    1.5953        0   1.09614      1.09614     0   
model      1.62443   1.62443  0.941912  1.09614         0            0     0   
dataset    1.62443   1.62443  0.941912  1.09614         0  9.31323e-09     0   
class            0         0         0        0         0            0     0   
perform          0         0         0        0         0            0     0   
learn      1.62443   1.62443  0.941912  1.09614         0  6.58545e-09     0   
detect     1.62443   1.62443  0.941912  1.09614         0            0     0   
use        1.62443   1.62443  0.941912  1.09614         0            0     0   
base       1.62443   1.62443  0.941912  1.09614         0  6.58545e-09     0   

         perform        learn       detect          use         base  
support        0      2.99493      2.99493      2.99493      2.99493  
granada        0      1.88382      1.88382      1.88382      1.88382  
covid          0        2.676        2.676        2.676        2.676  
research       0      2.07496      2.07496      2.07496      2.07496  
programm       0      2.07496      2.07496      2.07496      2.07496  
tabik          0      1.41287      1.41287      1.41287      1.41287  
garcía         0     0.979511     0.979511     0.979511     0.979511  
decsai         0      1.62443      1.62443      1.62443      1.62443  
project        0      1.62443      1.62443      1.62443      1.62443  
gómez          0     0.941912     0.941912     0.941912     0.941912  
imag           0      1.09614      1.09614      1.09614      1.09614  
model          0            0            0            0            0  
dataset        0  6.58545e-09            0            0  6.58545e-09  
class          0            0            0            0            0  
perform        0            0            0            0            0  
learn          0  9.31323e-09  6.58545e-09  6.58545e-09  9.31323e-09  
detect         0  6.58545e-09            0            0  6.58545e-09  
use            0  6.58545e-09            0            0  6.58545e-09  
base           0  9.31323e-09  6.58545e-09  6.58545e-09  9.31323e-09  

(0.13258127075526377, 0.2438, 0.7244119003984184)

mantel_test_verticle(model_science_articles,model_science_paragraph,10)

Pairwise-distance
           covid     imag        model    detect  dataset    learn       use  \
covid          0  2.33945      2.16792   1.61978   2.3276  1.89994   1.37432   
imag     2.33945        0       2.7422   1.75983  2.01837  2.33698   1.89132   
model    2.16792   2.7422  2.72057e-08   1.68726  2.37406   2.1051   1.88147   
detect   1.61978  1.75983      1.68726         0  1.79884   1.6455  0.984045   
dataset   2.3276  2.01837      2.37406   1.79884        0  2.01687   1.58888   
learn    1.89994  2.33698       2.1051    1.6455  2.01687        0   1.36097   
use      1.37432  1.89132      1.88147  0.984045  1.58888  1.36097         0   
class    1.44823  2.18102      2.13815   1.49212  1.87763    1.785   1.34592   
base     1.30498  1.86736       1.7136  0.705965  1.67631  1.40083  0.644773   
chest    1.58257  1.71112      1.88322  0.887614  1.79825  1.51976  0.806383   
perform  1.68452  2.09899      1.42377   1.10838   1.7877  1.49132  0.963038   

           class         base     chest      perform  
covid    1.44823      1.30498   1.58257      1.68452  
imag     2.18102      1.86736   1.71112      2.09899  
model    2.13815       1.7136   1.88322      1.42377  
detect   1.49212     0.705965  0.887614      1.10838  
dataset  1.87763      1.67631   1.79825       1.7877  
learn      1.785      1.40083   1.51976      1.49132  
use      1.34592     0.644773  0.806383     0.963038  
class          0       1.2082   1.27703      1.61368  
base      1.2082  9.61866e-09  0.783388      1.00457  
chest    1.27703     0.783388         0      1.08675  
perform  1.61368      1.00457   1.08675  1.92373e-08  
Pairwise-distance
           covid         imag    model   detect      dataset        learn  \
covid          0      1.40496  1.40555   1.3692       1.4051      1.39767   
imag     1.40496  2.10734e-08  1.40438  1.37789      1.40624      1.39621   
model    1.40555      1.40438        0  1.37233      1.40535      1.39539   
detect    1.3692      1.37789  1.37233        0      1.37291      1.36974   
dataset   1.4051      1.40624  1.40535  1.37291  2.58096e-08      1.39554   
learn    1.39767      1.39621  1.39539  1.36974      1.39554  2.10734e-08   
use      1.40336      1.40323  1.40291  1.37004      1.40296      1.39513   
class    1.40077      1.40121  1.40127   1.3683      1.40167      1.39051   
base     1.39171      1.39219  1.39106  1.36252      1.39153      1.37923   
chest    1.35387      1.34247  1.34782  1.31881       1.3413      1.32913   
perform  1.39974      1.39938  1.40094  1.37039      1.39905      1.39225   

                 use        class     base    chest      perform  
covid        1.40336      1.40077  1.39171  1.35387      1.39974  
imag         1.40323      1.40121  1.39219  1.34247      1.39938  
model        1.40291      1.40127  1.39106  1.34782      1.40094  
detect       1.37004       1.3683  1.36252  1.31881      1.37039  
dataset      1.40296      1.40167  1.39153   1.3413      1.39905  
learn        1.39513      1.39051  1.37923  1.32913      1.39225  
use      1.49012e-08      1.40024   1.3913     1.34      1.40017  
class        1.40024  1.49012e-08  1.39123  1.33915      1.39581  
base          1.3913      1.39123        0  1.32714      1.38573  
chest           1.34      1.33915  1.32714        0      1.33941  
perform      1.40017      1.39581  1.38573  1.33941  2.10734e-08  

(0.49915675662715714, 0.0257, 1.6653415184984917)

mantel_test_verticle(model_science_p,model_science_paragraph,10)

Pairwise-distance
            covid     imag     model       detect      dataset        learn  \
covid           0  1.60718     2.676        2.676        2.676        2.676   
imag      1.60718        0   1.09614      1.09614      1.09614      1.09614   
model       2.676  1.09614         0            0            0            0   
detect      2.676  1.09614         0            0            0  6.58545e-09   
dataset     2.676  1.09614         0            0  9.31323e-09  6.58545e-09   
learn       2.676  1.09614         0  6.58545e-09  6.58545e-09  9.31323e-09   
use         2.676  1.09614         0            0            0  6.58545e-09   
class           0        0         0            0            0            0   
base        2.676  1.09614         0  6.58545e-09  6.58545e-09  9.31323e-09   
chest     2.13813  0.54807   0.54807      0.54807      0.54807      0.54807   
support   4.44755  3.56952   2.99493      2.99493      2.99493      2.99493   
granada   3.34914  2.37968   1.88382      1.88382      1.88382      1.88382   
research  3.85527  2.76062   2.07496      2.07496      2.07496      2.07496   
programm  3.85527  2.76062   2.07496      2.07496      2.07496      2.07496   
tabik     3.08827  1.97031   1.41287      1.41287      1.41287      1.41287   
garcía    2.34183  1.18984  0.979511     0.979511     0.979511     0.979511   
decsai    3.61521  2.39296   1.62443      1.62443      1.62443      1.62443   
project   3.61521  2.39296   1.62443      1.62443      1.62443      1.62443   
gómez     2.88126   1.5953  0.941912     0.941912     0.941912     0.941912   

                  use class         base     chest   support   granada  \
covid           2.676     0        2.676   2.13813   4.44755   3.34914   
imag          1.09614     0      1.09614   0.54807   3.56952   2.37968   
model               0     0            0   0.54807   2.99493   1.88382   
detect              0     0  6.58545e-09   0.54807   2.99493   1.88382   
dataset             0     0  6.58545e-09   0.54807   2.99493   1.88382   
learn     6.58545e-09     0  9.31323e-09   0.54807   2.99493   1.88382   
use                 0     0  6.58545e-09   0.54807   2.99493   1.88382   
class               0     0            0         0         0         0   
base      6.58545e-09     0  9.31323e-09   0.54807   2.99493   1.88382   
chest         0.54807     0      0.54807         0   3.24887   2.07496   
support       2.99493     0      2.99493   3.24887         0   1.18984   
granada       1.88382     0      1.88382   2.07496   1.18984         0   
research      2.07496     0      2.07496   2.37968  0.941912   0.54807   
programm      2.07496     0      2.07496   2.37968  0.941912   0.54807   
tabik         1.41287     0      1.41287   1.62443   1.62443  0.470956   
garcía       0.979511     0     0.979511  0.941912   2.37968   1.18984   
decsai        1.62443     0      1.62443   1.97031   1.41287  0.638836   
project       1.62443     0      1.62443   1.97031   1.41287  0.638836   
gómez        0.941912     0     0.941912   1.18984   2.07496  0.941912   

          research  programm     tabik    garcía    decsai   project     gómez  
covid      3.85527   3.85527   3.08827   2.34183   3.61521   3.61521   2.88126  
imag       2.76062   2.76062   1.97031   1.18984   2.39296   2.39296    1.5953  
model      2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
detect     2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
dataset    2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
learn      2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
use        2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
class            0         0         0         0         0         0         0  
base       2.07496   2.07496   1.41287  0.979511   1.62443   1.62443  0.941912  
chest      2.37968   2.37968   1.62443  0.941912   1.97031   1.97031   1.18984  
support   0.941912  0.941912   1.62443   2.37968   1.41287   1.41287   2.07496  
granada    0.54807   0.54807  0.470956   1.18984  0.638836  0.638836  0.941912  
research         0         0  0.797652    1.5953  0.470956  0.470956   1.18984  
programm         0         0  0.797652    1.5953  0.470956  0.470956   1.18984  
tabik     0.797652  0.797652         0  0.797652   0.54807   0.54807  0.470956  
garcía      1.5953    1.5953  0.797652         0   1.28509   1.28509   0.54807  
decsai    0.470956  0.470956   0.54807   1.28509         0         0  0.797652  
project   0.470956  0.470956   0.54807   1.28509         0         0  0.797652  
gómez      1.18984   1.18984  0.470956   0.54807  0.797652  0.797652         0  
Pairwise-distance
             covid         imag     model    detect      dataset        learn  \
covid            0      1.40496   1.40555    1.3692       1.4051      1.39767   
imag       1.40496  2.10734e-08   1.40438   1.37789      1.40624      1.39621   
model      1.40555      1.40438         0   1.37233      1.40535      1.39539   
detect      1.3692      1.37789   1.37233         0      1.37291      1.36974   
dataset     1.4051      1.40624   1.40535   1.37291  2.58096e-08      1.39554   
learn      1.39767      1.39621   1.39539   1.36974      1.39554  2.10734e-08   
use        1.40336      1.40323   1.40291   1.37004      1.40296      1.39513   
class      1.40077      1.40121   1.40127    1.3683      1.40167      1.39051   
base       1.39171      1.39219   1.39106   1.36252      1.39153      1.37923   
chest      1.35387      1.34247   1.34782   1.31881       1.3413      1.32913   
support    1.03992      1.04488   1.04005   1.01011      1.04274      1.04058   
granada   0.995027     0.993812  0.994144  0.948831     0.994089     0.981639   
research   1.02313      1.01966   1.02601  0.962638      1.02547      1.01492   
programm  0.994901     0.993977  0.994012  0.949953     0.993727     0.982201   
tabik     0.987813     0.991898  0.998183  0.959294     0.985325      0.98497   
garcía    0.994578      0.99333  0.993713  0.948831     0.993357     0.981242   
decsai    0.994572     0.993338  0.993544   0.94882     0.993303     0.981023   
project    1.00202     0.999023   1.00121  0.953546     0.998148     0.991151   
gómez     0.994565     0.993426  0.993702  0.949039     0.993318     0.981266   

                  use        class      base     chest   support      granada  \
covid         1.40336      1.40077   1.39171   1.35387   1.03992     0.995027   
imag          1.40323      1.40121   1.39219   1.34247   1.04488     0.993812   
model         1.40291      1.40127   1.39106   1.34782   1.04005     0.994144   
detect        1.37004       1.3683   1.36252   1.31881   1.01011     0.948831   
dataset       1.40296      1.40167   1.39153    1.3413   1.04274     0.994089   
learn         1.39513      1.39051   1.37923   1.32913   1.04058     0.981639   
use       1.49012e-08      1.40024    1.3913      1.34   1.04288     0.990765   
class         1.40024  1.49012e-08   1.39123   1.33915   1.03416     0.988247   
base           1.3913      1.39123         0   1.32714   1.02338      0.97343   
chest            1.34      1.33915   1.32714         0   0.94813      0.90672   
support       1.04288      1.03416   1.02338   0.94813         0     0.310449   
granada      0.990765     0.988247   0.97343   0.90672  0.310449  4.65661e-10   
research      1.02154      1.01538  0.995403  0.935802  0.328554     0.240846   
programm     0.991484      0.98795  0.973484  0.907467  0.287824     0.032859   
tabik        0.996508     0.991715  0.954761  0.878189  0.312154    0.0595399   
garcía       0.990912     0.987709  0.973212  0.907908  0.310243    0.0226287   
decsai        0.99085     0.987747  0.973076  0.907984  0.309628    0.0222633   
project      0.999875     0.990083  0.979392  0.917089  0.299808     0.113611   
gómez        0.990972     0.987726  0.973207  0.907813  0.306612     0.022988   

             research   programm      tabik      garcía      decsai   project  \
covid         1.02313   0.994901   0.987813    0.994578    0.994572   1.00202   
imag          1.01966   0.993977   0.991898     0.99333    0.993338  0.999023   
model         1.02601   0.994012   0.998183    0.993713    0.993544   1.00121   
detect       0.962638   0.949953   0.959294    0.948831     0.94882  0.953546   
dataset       1.02547   0.993727   0.985325    0.993357    0.993303  0.998148   
learn         1.01492   0.982201    0.98497    0.981242    0.981023  0.991151   
use           1.02154   0.991484   0.996508    0.990912     0.99085  0.999875   
class         1.01538    0.98795   0.991715    0.987709    0.987747  0.990083   
base         0.995403   0.973484   0.954761    0.973212    0.973076  0.979392   
chest        0.935802   0.907467   0.878189    0.907908    0.907984  0.917089   
support      0.328554   0.287824   0.312154    0.310243    0.309628  0.299808   
granada      0.240846   0.032859  0.0595399   0.0226287   0.0222633  0.113611   
research  7.45058e-09   0.233028   0.252747    0.243118    0.243634  0.248982   
programm     0.233028          0  0.0607255   0.0244146   0.0233586  0.107361   
tabik        0.252747  0.0607255          0   0.0561834    0.056703  0.121669   
garcía       0.243118  0.0244146  0.0561834           0   0.0061094  0.109594   
decsai       0.243634  0.0233586   0.056703   0.0061094           0  0.109892   
project      0.248982   0.107361   0.121669    0.109594    0.109892         0   
gómez        0.242569  0.0206644  0.0560565  0.00439174  0.00571723   0.10894   

               gómez  
covid       0.994565  
imag        0.993426  
model       0.993702  
detect      0.949039  
dataset     0.993318  
learn       0.981266  
use         0.990972  
class       0.987726  
base        0.973207  
chest       0.907813  
support     0.306612  
granada     0.022988  
research    0.242569  
programm   0.0206644  
tabik      0.0560565  
garcía    0.00439174  
decsai    0.00571723  
project      0.10894  
gómez              0  

(0.029764461120292685, 0.4411, 0.18913563513179427)

mantel_test_verticle(model_psy_articles,model_psy_p,10)

Pairwise-distance
             misinfodem    covid    articl   health    mechan    commun  \
misinfodem  1.36028e-08  1.59423  0.902727  1.69295  0.492125  0.557808   
covid           1.59423        0   1.46013  2.06287   1.47337   1.50049   
articl         0.902727  1.46013         0   1.5335  0.550356  0.798937   
health          1.69295  2.06287    1.5335        0    1.6193   1.62403   
mechan         0.492125  1.47337  0.550356   1.6193         0  0.433102   
commun         0.557808  1.50049  0.798937  1.62403  0.433102         0   
psycholog       1.57397  1.71712   1.23458  1.66727   1.33457   1.25524   
strategi       0.408792  1.49023  0.606565  1.59347    0.1669  0.383342   
misinform      0.258613  1.51955  0.694399  1.63517  0.234499  0.425667   
inform         0.476618  1.49355  0.735753  1.72274  0.389701  0.349695   
group            2.1644  2.45244   1.96966  2.61359   2.01066   2.16182   
pandem          1.12183  1.32272  0.770845  1.45061  0.949488   1.10867   
peopl           1.42213  1.86567   1.23285  2.18477   1.28403   1.29756   
impact          1.06517  1.43686  0.541267  1.70716  0.872451   1.07137   
social         0.960577   1.6669    1.1423  1.78312  0.944618  0.952591   
studi           1.05214  1.10668  0.992271  1.85649  0.806163  0.926028   
research        1.57553  1.85642   1.46791  2.14506   1.40045     1.216   

           psycholog  strategi misinform    inform    group    pandem  \
misinfodem   1.57397  0.408792  0.258613  0.476618   2.1644   1.12183   
covid        1.71712   1.49023   1.51955   1.49355  2.45244   1.32272   
articl       1.23458  0.606565  0.694399  0.735753  1.96966  0.770845   
health       1.66727   1.59347   1.63517   1.72274  2.61359   1.45061   
mechan       1.33457    0.1669  0.234499  0.389701  2.01066  0.949488   
commun       1.25524  0.383342  0.425667  0.349695  2.16182   1.10867   
psycholog          0   1.34872   1.42905   1.36647  2.36585   1.29309   
strategi     1.34872         0  0.180432  0.378132  2.04866  0.975622   
misinform    1.42905  0.180432         0  0.352715   2.0764   1.00827   
inform       1.36647  0.378132  0.352715         0   2.0741    1.0411   
group        2.36585   2.04866    2.0764    2.0741        0   2.24513   
pandem       1.29309  0.975622   1.00827    1.0411  2.24513         0   
peopl        1.77965   1.32853   1.32846   1.13912  2.00935   1.51763   
impact       1.38994  0.878036  0.933853   1.02881  2.18441  0.875398   
social       1.56908  0.953546  0.920866  0.761349  1.89647   1.34763   
studi        1.36025   0.83285  0.903747  0.994846   2.0032   1.23384   
research     1.19442   1.37128   1.46212   1.41718  2.39065    1.7469   

              peopl       impact    social     studi research  
misinfodem  1.42213      1.06517  0.960577   1.05214  1.57553  
covid       1.86567      1.43686    1.6669   1.10668  1.85642  
articl      1.23285     0.541267    1.1423  0.992271  1.46791  
health      2.18477      1.70716   1.78312   1.85649  2.14506  
mechan      1.28403     0.872451  0.944618  0.806163  1.40045  
commun      1.29756      1.07137  0.952591  0.926028    1.216  
psycholog   1.77965      1.38994   1.56908   1.36025  1.19442  
strategi    1.32853     0.878036  0.953546   0.83285  1.37128  
misinform   1.32846     0.933853  0.920866  0.903747  1.46212  
inform      1.13912      1.02881  0.761349  0.994846  1.41718  
group       2.00935      2.18441   1.89647    2.0032  2.39065  
pandem      1.51763     0.875398   1.34763   1.23384   1.7469  
peopl             0      1.48974   1.23727   1.68566  1.97039  
impact      1.48974  1.36028e-08   1.41787    1.0847  1.54162  
social      1.23727      1.41787         0    1.2903  1.68404  
studi       1.68566       1.0847    1.2903         0  1.26559  
research    1.97039      1.54162   1.68404   1.26559        0  
Pairwise-distance
           misinfodem    covid    articl    health    mechan    commun  \
misinfodem          0  1.35287   2.09875   2.98805   2.98805   2.98805   
covid         1.35287        0   1.13552   1.67187   1.67187   1.67187   
articl        2.09875  1.13552         0   1.22885   1.22885   1.22885   
health        2.98805  1.67187   1.22885         0         0         0   
mechan        2.98805  1.67187   1.22885         0         0         0   
commun        2.98805  1.67187   1.22885         0         0         0   
psycholog     2.58051  1.70328  0.567759   1.35447   1.35447   1.35447   
strategi      2.58051  1.70328  0.567759   1.35447   1.35447   1.35447   
misinform     3.60765  2.25478    2.0571  0.841095  0.841095  0.841095   
inform        2.91809  1.72034  0.901913  0.558152  0.558152  0.558152   
group               0        0         0         0         0         0   
pandem        4.05861  2.70574    2.4577   1.22885   1.22885   1.22885   
peopl         2.91809  1.72034  0.901913  0.558152  0.558152  0.558152   
impact        3.34632    2.236   1.25066    1.0317    1.0317    1.0317   
social        4.05861  2.70574    2.4577   1.22885   1.22885   1.22885   
studi               0        0         0         0         0         0   
research      3.77527  2.50132   1.80383  0.860169  0.860169  0.860169   

           psycholog  strategi misinform       inform group       pandem  \
misinfodem   2.58051   2.58051   3.60765      2.91809     0      4.05861   
covid        1.70328   1.70328   2.25478      1.72034     0      2.70574   
articl      0.567759  0.567759    2.0571     0.901913     0       2.4577   
health       1.35447   1.35447  0.841095     0.558152     0      1.22885   
mechan       1.35447   1.35447  0.841095     0.558152     0      1.22885   
commun       1.35447   1.35447  0.841095     0.558152     0      1.22885   
psycholog          0         0   2.18461     0.841095     0      2.52328   
strategi           0         0   2.18461     0.841095     0      2.52328   
misinform    2.18461   2.18461         0      1.35447     0     0.450957   
inform      0.841095  0.841095   1.35447            0     0      1.68219   
group              0         0         0            0     0            0   
pandem       2.52328   2.52328  0.450957      1.68219     0  2.63418e-08   
peopl       0.841095  0.841095   1.35447  9.31323e-09     0      1.68219   
impact      0.901913  0.901913   1.67445     0.567759     0      1.89336   
social       2.52328   2.52328  0.450957      1.68219     0  2.63418e-08   
studi              0         0         0            0     0            0   
research     1.64907   1.64907    1.0317     0.901913     0       1.1163   

                  peopl    impact       social studi     research  
misinfodem      2.91809   3.34632      4.05861     0      3.77527  
covid           1.72034     2.236      2.70574     0      2.50132  
articl         0.901913   1.25066       2.4577     0      1.80383  
health         0.558152    1.0317      1.22885     0     0.860169  
mechan         0.558152    1.0317      1.22885     0     0.860169  
commun         0.558152    1.0317      1.22885     0     0.860169  
psycholog      0.841095  0.901913      2.52328     0      1.64907  
strategi       0.841095  0.901913      2.52328     0      1.64907  
misinform       1.35447   1.67445     0.450957     0       1.0317  
inform      9.31323e-09  0.567759      1.68219     0     0.901913  
group                 0         0            0     0            0  
pandem          1.68219   1.89336  2.63418e-08     0       1.1163  
peopl                 0  0.567759      1.68219     0     0.901913  
impact         0.567759         0      1.89336     0     0.841095  
social          1.68219   1.89336  2.63418e-08     0       1.1163  
studi                 0         0            0     0            0  
research       0.901913  0.841095       1.1163     0  9.31323e-09  

(-0.2884071564199686, 0.9284, -1.4702499292889604)

mantel_test_verticle(model_psy_articles,model_psy_paragraph,10)

Pairwise-distance
             covid    pandem   health    polici       impact      countri  \
covid            0   1.32272  2.06287   1.47845      1.43686      1.19061   
pandem     1.32272         0  1.45061  0.840065     0.875398      1.37045   
health     2.06287   1.45061        0    1.6505      1.70716      2.08939   
polici     1.47845  0.840065   1.6505         0     0.609937      1.16456   
impact     1.43686  0.875398  1.70716  0.609937  1.36028e-08      1.39973   
countri    1.19061   1.37045  2.08939   1.16456      1.39973  1.92373e-08   
develop     1.5276  0.774868  1.61764  0.382845     0.510095      1.26566   
nation     1.52618  0.901196   1.5652  0.410922      0.73547      1.21104   
sustain     1.5939  0.879967  1.78022  0.455998     0.405692      1.31942   
economi    1.48395   0.82434  1.74574  0.369323     0.417327       1.2103   
psycholog  1.71712   1.29309  1.66727   1.20742      1.38994      1.56731   
group      2.45244   2.24513  2.61359   2.05155      2.18441      2.41425   
peopl      1.86567   1.51763  2.18477   1.24316      1.48974      1.76839   
social      1.6669   1.34763  1.78312   1.10936      1.41787      1.49008   
studi      1.10668   1.23384  1.85649    0.9031       1.0847     0.885375   
research   1.85642    1.7469  2.14506   1.38514      1.54162      1.40456   

            develop       nation      sustain      economi psycholog    group  \
covid        1.5276      1.52618       1.5939      1.48395   1.71712  2.45244   
pandem     0.774868     0.901196     0.879967      0.82434   1.29309  2.24513   
health      1.61764       1.5652      1.78022      1.74574   1.66727  2.61359   
polici     0.382845     0.410922     0.455998     0.369323   1.20742  2.05155   
impact     0.510095      0.73547     0.405692     0.417327   1.38994  2.18441   
countri     1.26566      1.21104      1.31942       1.2103   1.56731  2.41425   
develop           0     0.612948     0.332283     0.308344   1.23001  2.10616   
nation     0.612948  6.80142e-09     0.665881     0.572731   1.36464   2.0783   
sustain    0.332283     0.665881  1.36028e-08      0.17173    1.3954  2.14035   
economi    0.308344     0.572731      0.17173  9.61866e-09   1.38274  2.13791   
psycholog   1.23001      1.36464       1.3954      1.38274         0  2.36585   
group       2.10616       2.0783      2.14035      2.13791   2.36585        0   
peopl       1.33626      1.27791      1.34347      1.30761   1.77965  2.00935   
social      1.19599      1.09806      1.34228      1.29889   1.56908  1.89647   
studi       1.06748     0.943296      1.15987      1.04334   1.36025   2.0032   
research    1.49114      1.48786      1.58265      1.55864   1.19442  2.39065   

             peopl   social     studi research  
covid      1.86567   1.6669   1.10668  1.85642  
pandem     1.51763  1.34763   1.23384   1.7469  
health     2.18477  1.78312   1.85649  2.14506  
polici     1.24316  1.10936    0.9031  1.38514  
impact     1.48974  1.41787    1.0847  1.54162  
countri    1.76839  1.49008  0.885375  1.40456  
develop    1.33626  1.19599   1.06748  1.49114  
nation     1.27791  1.09806  0.943296  1.48786  
sustain    1.34347  1.34228   1.15987  1.58265  
economi    1.30761  1.29889   1.04334  1.55864  
psycholog  1.77965  1.56908   1.36025  1.19442  
group      2.00935  1.89647    2.0032  2.39065  
peopl            0  1.23727   1.68566  1.97039  
social     1.23727        0    1.2903  1.68404  
studi      1.68566   1.2903         0  1.26559  
research   1.97039  1.68404   1.26559        0  
Pairwise-distance
             covid   pandem       health    polici   impact      countri  \
covid            0  1.40424      1.39832   1.18101   1.3943      1.39452   
pandem     1.40424        0      1.39659   1.17943  1.39343      1.39162   
health     1.39832  1.39659  2.98023e-08   1.16945  1.38795      1.38601   
polici     1.18101  1.17943      1.16945         0  1.17588      1.17229   
impact      1.3943  1.39343      1.38795   1.17588        0      1.38641   
countri    1.39452  1.39162      1.38601   1.17229  1.38641  2.98023e-08   
develop    1.38326  1.38447      1.37717   1.15938  1.37587      1.36229   
nation     1.22899  1.22584      1.22499  0.837969  1.22654      1.23319   
sustain    1.35901   1.3518      1.34372   1.13024  1.34354      1.35344   
economi    1.36338  1.36026      1.35577   1.13045   1.3521      1.35641   
psycholog  1.40124  1.39922      1.39094   1.17297  1.39029      1.38934   
group      1.39464  1.39078      1.38352   1.16873   1.3845      1.38134   
peopl      1.40211  1.39901      1.39488   1.18087  1.39323      1.38996   
social     1.37457  1.36797      1.36012    1.1593  1.35911      1.35786   
studi       1.3996  1.39568      1.38864   1.16752   1.3894      1.38996   
research   1.38695  1.38805      1.37808   1.15817  1.37858      1.37654   

           develop       nation  sustain  economi    psycholog    group  \
covid      1.38326      1.22899  1.35901  1.36338      1.40124  1.39464   
pandem     1.38447      1.22584   1.3518  1.36026      1.39922  1.39078   
health     1.37717      1.22499  1.34372  1.35577      1.39094  1.38352   
polici     1.15938     0.837969  1.13024  1.13045      1.17297  1.16873   
impact     1.37587      1.22654  1.34354   1.3521      1.39029   1.3845   
countri    1.36229      1.23319  1.35344  1.35641      1.38934  1.38134   
develop          0      1.15853  1.30987  1.32839      1.37953   1.3717   
nation     1.15853  2.10734e-08  1.18406  1.23581      1.22554    1.214   
sustain    1.30987      1.18406        0  1.32734      1.33487  1.34745   
economi    1.32839      1.23581  1.32734        0      1.34889  1.34992   
psycholog  1.37953      1.22554  1.33487  1.34889  2.58096e-08  1.38857   
group       1.3717        1.214  1.34745  1.34992      1.38857        0   
peopl      1.37452      1.22695  1.35513  1.36081      1.39601  1.38894   
social     1.34745      1.18433  1.31744  1.31772      1.37095  1.35866   
studi      1.37523      1.21671  1.35226  1.35133      1.39514  1.38631   
research   1.36426      1.19852   1.3439  1.33826      1.38225  1.37627   

             peopl   social    studi     research  
covid      1.40211  1.37457   1.3996      1.38695  
pandem     1.39901  1.36797  1.39568      1.38805  
health     1.39488  1.36012  1.38864      1.37808  
polici     1.18087   1.1593  1.16752      1.15817  
impact     1.39323  1.35911   1.3894      1.37858  
countri    1.38996  1.35786  1.38996      1.37654  
develop    1.37452  1.34745  1.37523      1.36426  
nation     1.22695  1.18433  1.21671      1.19852  
sustain    1.35513  1.31744  1.35226       1.3439  
economi    1.36081  1.31772  1.35133      1.33826  
psycholog  1.39601  1.37095  1.39514      1.38225  
group      1.38894  1.35866  1.38631      1.37627  
peopl            0  1.37228  1.39474       1.3849  
social     1.37228        0  1.36679      1.35473  
studi      1.39474  1.36679        0      1.38393  
research    1.3849  1.35473  1.38393  1.49012e-08  

(0.4338699333866362, 0.0024, 1.8506050566886427)

mantel_test_verticle(model_psy_p,model_psy_paragraph,10)

Pairwise-distance
              covid       pandem    health polici       impact countri  \
covid             0      2.70574   1.67187      0        2.236       0   
pandem      2.70574  2.63418e-08   1.22885      0      1.89336       0   
health      1.67187      1.22885         0      0       1.0317       0   
polici            0            0         0      0            0       0   
impact        2.236      1.89336    1.0317      0            0       0   
countri           0            0         0      0            0       0   
develop       2.236      1.89336    1.0317      0  6.58545e-09       0   
nation            0            0         0      0            0       0   
sustain           0            0         0      0            0       0   
economi           0            0         0      0            0       0   
misinfodem  1.35287      4.05861   2.98805      0      3.34632       0   
articl      1.13552       2.4577   1.22885      0      1.25066       0   
mechan      1.67187      1.22885         0      0       1.0317       0   
commun      1.67187      1.22885         0      0       1.0317       0   
psycholog   1.70328      2.52328   1.35447      0     0.901913       0   
strategi    1.70328      2.52328   1.35447      0     0.901913       0   
misinform   2.25478     0.450957  0.841095      0      1.67445       0   
inform      1.72034      1.68219  0.558152      0     0.567759       0   

                develop nation sustain economi misinfodem    articl    mechan  \
covid             2.236      0       0       0    1.35287   1.13552   1.67187   
pandem          1.89336      0       0       0    4.05861    2.4577   1.22885   
health           1.0317      0       0       0    2.98805   1.22885         0   
polici                0      0       0       0          0         0         0   
impact      6.58545e-09      0       0       0    3.34632   1.25066    1.0317   
countri               0      0       0       0          0         0         0   
develop               0      0       0       0    3.34632   1.25066    1.0317   
nation                0      0       0       0          0         0         0   
sustain               0      0       0       0          0         0         0   
economi               0      0       0       0          0         0         0   
misinfodem      3.34632      0       0       0          0   2.09875   2.98805   
articl          1.25066      0       0       0    2.09875         0   1.22885   
mechan           1.0317      0       0       0    2.98805   1.22885         0   
commun           1.0317      0       0       0    2.98805   1.22885         0   
psycholog      0.901913      0       0       0    2.58051  0.567759   1.35447   
strategi       0.901913      0       0       0    2.58051  0.567759   1.35447   
misinform       1.67445      0       0       0    3.60765    2.0571  0.841095   
inform         0.567759      0       0       0    2.91809  0.901913  0.558152   

              commun psycholog  strategi misinform    inform  
covid        1.67187   1.70328   1.70328   2.25478   1.72034  
pandem       1.22885   2.52328   2.52328  0.450957   1.68219  
health             0   1.35447   1.35447  0.841095  0.558152  
polici             0         0         0         0         0  
impact        1.0317  0.901913  0.901913   1.67445  0.567759  
countri            0         0         0         0         0  
develop       1.0317  0.901913  0.901913   1.67445  0.567759  
nation             0         0         0         0         0  
sustain            0         0         0         0         0  
economi            0         0         0         0         0  
misinfodem   2.98805   2.58051   2.58051   3.60765   2.91809  
articl       1.22885  0.567759  0.567759    2.0571  0.901913  
mechan             0   1.35447   1.35447  0.841095  0.558152  
commun             0   1.35447   1.35447  0.841095  0.558152  
psycholog    1.35447         0         0   2.18461  0.841095  
strategi     1.35447         0         0   2.18461  0.841095  
misinform   0.841095   2.18461   2.18461         0   1.35447  
inform      0.558152  0.841095  0.841095   1.35447         0  
Pairwise-distance
              covid    pandem       health    polici    impact      countri  \
covid             0   1.40424      1.39832   1.18101    1.3943      1.39452   
pandem      1.40424         0      1.39659   1.17943   1.39343      1.39162   
health      1.39832   1.39659  2.98023e-08   1.16945   1.38795      1.38601   
polici      1.18101   1.17943      1.16945         0   1.17588      1.17229   
impact       1.3943   1.39343      1.38795   1.17588         0      1.38641   
countri     1.39452   1.39162      1.38601   1.17229   1.38641  2.98023e-08   
develop     1.38326   1.38447      1.37717   1.15938   1.37587      1.36229   
nation      1.22899   1.22584      1.22499  0.837969   1.22654      1.23319   
sustain     1.35901    1.3518      1.34372   1.13024   1.34354      1.35344   
economi     1.36338   1.36026      1.35577   1.13045    1.3521      1.35641   
misinfodem  1.01939   1.01529      1.02419  0.688077   1.01745      1.01177   
articl       1.3135   1.30856      1.30736   1.07248   1.30274      1.30487   
mechan      0.99498  0.993759     0.985503  0.638435  0.985301     0.979537   
commun      1.37027   1.36762      1.35845   1.15846   1.35994      1.35824   
psycholog   1.40124   1.39922      1.39094   1.17297   1.39029      1.38934   
strategi    1.03041   1.02139       1.0143  0.697534    1.0183     0.999283   
misinform   1.00806   1.00548     0.989343   0.65129  0.995768     0.991685   
inform      1.37625   1.37323      1.36722   1.14505   1.36564        1.364   

             develop       nation   sustain   economi   misinfodem    articl  \
covid        1.38326      1.22899   1.35901   1.36338      1.01939    1.3135   
pandem       1.38447      1.22584    1.3518   1.36026      1.01529   1.30856   
health       1.37717      1.22499   1.34372   1.35577      1.02419   1.30736   
polici       1.15938     0.837969   1.13024   1.13045     0.688077   1.07248   
impact       1.37587      1.22654   1.34354    1.3521      1.01745   1.30274   
countri      1.36229      1.23319   1.35344   1.35641      1.01177   1.30487   
develop            0      1.15853   1.30987   1.32839      1.00215   1.29744   
nation       1.15853  2.10734e-08   1.18406   1.23581     0.733643   1.10257   
sustain      1.30987      1.18406         0   1.32734     0.952904   1.24464   
economi      1.32839      1.23581   1.32734         0     0.956511   1.24344   
misinfodem   1.00215     0.733643  0.952904  0.956511  7.45058e-09   0.87454   
articl       1.29744      1.10257   1.24464   1.24344      0.87454         0   
mechan      0.963167     0.722768   0.92784  0.930869     0.216625  0.862229   
commun       1.34383      1.18786   1.31982   1.32736     0.991352   1.26474   
psycholog    1.37953      1.22554   1.33487   1.34889      1.02037   1.32411   
strategi    0.940785     0.755852  0.962041  0.978575     0.264792  0.885626   
misinform   0.976789     0.742552  0.945968  0.951166     0.210444  0.883107   
inform       1.34691      1.21359   1.32984   1.33552     0.908467   1.28782   

              mechan       commun    psycholog     strategi misinform  \
covid        0.99498      1.37027      1.40124      1.03041   1.00806   
pandem      0.993759      1.36762      1.39922      1.02139   1.00548   
health      0.985503      1.35845      1.39094       1.0143  0.989343   
polici      0.638435      1.15846      1.17297     0.697534   0.65129   
impact      0.985301      1.35994      1.39029       1.0183  0.995768   
countri     0.979537      1.35824      1.38934     0.999283  0.991685   
develop     0.963167      1.34383      1.37953     0.940785  0.976789   
nation      0.722768      1.18786      1.22554     0.755852  0.742552   
sustain      0.92784      1.31982      1.33487     0.962041  0.945968   
economi     0.930869      1.32736      1.34889     0.978575  0.951166   
misinfodem  0.216625     0.991352      1.02037     0.264792  0.210444   
articl      0.862229      1.26474      1.32411     0.885626  0.883107   
mechan             0     0.948595     0.988374     0.243203  0.142187   
commun      0.948595  1.49012e-08      1.36425      0.96571  0.939483   
psycholog   0.988374      1.36425  2.58096e-08      1.02155  0.999707   
strategi    0.243203      0.96571      1.02155  3.72529e-09  0.232545   
misinform   0.142187     0.939483     0.999707     0.232545         0   
inform      0.922925      1.33091      1.37231     0.984985  0.948071   

              inform  
covid        1.37625  
pandem       1.37323  
health       1.36722  
polici       1.14505  
impact       1.36564  
countri        1.364  
develop      1.34691  
nation       1.21359  
sustain      1.32984  
economi      1.33552  
misinfodem  0.908467  
articl       1.28782  
mechan      0.922925  
commun       1.33091  
psycholog    1.37231  
strategi    0.984985  
misinform   0.948071  
inform             0  

(-0.22712002342760387, 0.8713, -1.1712869238069015)